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Tracking Semantic Change in Slovene: A Novel Dataset and Optimal Transport-Based Distance (2402.16596v2)
Published 26 Feb 2024 in cs.CL
Abstract: In this paper, we focus on the detection of semantic changes in Slovene, a less resourced Slavic language with two million speakers. Detecting and tracking semantic changes provides insight into the evolution of language caused by changes in society and culture. We present the first Slovene dataset for evaluating semantic change detection systems, which contains aggregated semantic change scores for 104 target words obtained from more than 3,000 manually annotated sentence pairs. We analyze an important class of measures of semantic change metrics based on the Average pairwise distance and identify several limitations. To address these limitations, we propose a novel metric based on regularized optimal transport, which offers a more robust framework for quantifying semantic change. We provide a comprehensive evaluation of various existing semantic change detection methods and associated semantic change measures on our dataset. Through empirical testing, we demonstrate that our proposed approach, leveraging regularized optimal transport, achieves either matching or improved performance compared to baseline approaches.
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In: Proceedings of the EACL Hackashop on News Media Content Analysis and Automated Report Generation, pp. 121–126 (2021) Montariol et al. [2021] Montariol, S., Martinc, M., Pivovarova, L., et al.: Scalable and interpretable semantic change detection. In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics Human Language Technologies (2021). The Association for Computational Linguistics Stewart et al. [2017] Stewart, I., Arendt, D., Bell, E., Volkova, S.: Measuring, predicting and visualizing short-term change in word representation and usage in VKontakte social network. In: Eleventh International AAAI Conference on Web and Social Media (2017) Gulordava and Baroni [2011] Gulordava, K., Baroni, M.: A distributional similarity approach to the detection of semantic change in the Google Books ngram corpus. In: Pado, S., Peirsman, Y. (eds.) 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Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. 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[2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Juola, P.: The time course of language change. Computers and the Humanities 37(1), 77–96 (2003) Hilpert and Gries [2008] Hilpert, M., Gries, S.T.: Assessing frequency changes in multistage diachronic corpora: Applications for historical corpus linguistics and the study of language acquisition. Literary and Linguistic Computing 24(4), 385–401 (2008) Mikolov et al. [2013] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Kutuzov et al. [2018] Kutuzov, A., Øvrelid, L., Szymanski, T., Velldal, E.: Diachronic word embeddings and semantic shifts: a survey. In: Bender, E.M., Derczynski, L., Isabelle, P. (eds.) Proceedings of the 27th International Conference on Computational Linguistics, pp. 1384–1397. Association for Computational Linguistics, Santa Fe, New Mexico, USA (2018). https://aclanthology.org/C18-1117 Tang [2018] Tang, X.: A state-of-the-art of semantic change computation. Natural Language Engineering 24(5), 649–676 (2018) Kim et al. [2014] Kim, Y., Chiu, Y.-I., Hanaki, K., Hegde, D., Petrov, S.: Temporal analysis of language through neural language models. In: Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, pp. 61–65 (2014). https://doi.org/10.3115/v1/W14-2517 . http://aclweb.org/anthology/W14-2517 Hamilton et al. [2016a] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Diachronic word embeddings reveal statistical laws of semantic change. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 1489–1501 (2016). https://doi.org/10.18653/v1/P16-1141 . http://aclweb.org/anthology/P16-1141 Hamilton et al. [2016b] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Cultural shift or linguistic drift? comparing two computational measures of semantic change. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2116–2121. Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1229 . https://www.aclweb.org/anthology/D16-1229 Yin et al. [2018] Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. 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In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Kutuzov et al. [2018] Kutuzov, A., Øvrelid, L., Szymanski, T., Velldal, E.: Diachronic word embeddings and semantic shifts: a survey. In: Bender, E.M., Derczynski, L., Isabelle, P. (eds.) Proceedings of the 27th International Conference on Computational Linguistics, pp. 1384–1397. Association for Computational Linguistics, Santa Fe, New Mexico, USA (2018). https://aclanthology.org/C18-1117 Tang [2018] Tang, X.: A state-of-the-art of semantic change computation. Natural Language Engineering 24(5), 649–676 (2018) Kim et al. 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In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. 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[2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. 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[2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. 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[2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. 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[2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. 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[2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. 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[2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. 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In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. 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Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. 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[2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. 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[2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. 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Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. 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Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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[2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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[2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. 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In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. 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[2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. 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Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. 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Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. 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In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. 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In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. 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Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.lchange-1.2 . https://aclanthology.org/2021.lchange-1.2 Rodina and Kutuzov [2020] Rodina, J., Kutuzov, A.: RuSemShift: a dataset of historical lexical semantic change in Russian. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 1037–1047. International Committee on Computational Linguistics, Barcelona, Spain (Online) (2020). https://doi.org/10.18653/v1/2020.coling-main.90 . https://aclanthology.org/2020.coling-main.90 Martinc et al. [2022] Martinc, M., Dobrovoljc, K., Pollak, S.: Semantic change detection datasets for Slovenian 1.0. Slovenian language resource repository CLARIN.SI (2022). http://hdl.handle.net/11356/1651 Juola [2003] Juola, P.: The time course of language change. 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[2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. 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[2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. 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In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Juola, P.: The time course of language change. Computers and the Humanities 37(1), 77–96 (2003) Hilpert and Gries [2008] Hilpert, M., Gries, S.T.: Assessing frequency changes in multistage diachronic corpora: Applications for historical corpus linguistics and the study of language acquisition. Literary and Linguistic Computing 24(4), 385–401 (2008) Mikolov et al. [2013] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Kutuzov et al. [2018] Kutuzov, A., Øvrelid, L., Szymanski, T., Velldal, E.: Diachronic word embeddings and semantic shifts: a survey. In: Bender, E.M., Derczynski, L., Isabelle, P. (eds.) Proceedings of the 27th International Conference on Computational Linguistics, pp. 1384–1397. Association for Computational Linguistics, Santa Fe, New Mexico, USA (2018). https://aclanthology.org/C18-1117 Tang [2018] Tang, X.: A state-of-the-art of semantic change computation. Natural Language Engineering 24(5), 649–676 (2018) Kim et al. [2014] Kim, Y., Chiu, Y.-I., Hanaki, K., Hegde, D., Petrov, S.: Temporal analysis of language through neural language models. In: Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, pp. 61–65 (2014). https://doi.org/10.3115/v1/W14-2517 . http://aclweb.org/anthology/W14-2517 Hamilton et al. [2016a] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Diachronic word embeddings reveal statistical laws of semantic change. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 1489–1501 (2016). https://doi.org/10.18653/v1/P16-1141 . http://aclweb.org/anthology/P16-1141 Hamilton et al. [2016b] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Cultural shift or linguistic drift? comparing two computational measures of semantic change. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2116–2121. Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1229 . https://www.aclweb.org/anthology/D16-1229 Yin et al. [2018] Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. 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Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. 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[2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. 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[2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. 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[2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. 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[2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. 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[2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. 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[2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. 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In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. 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Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. 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[2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. 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[2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. 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Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. 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Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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[2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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[2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. 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In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. 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[2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. 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Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. 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Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. 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In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. 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In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. 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International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. 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[2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. 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Natural Language Engineering 24(5), 649–676 (2018) Kim et al. [2014] Kim, Y., Chiu, Y.-I., Hanaki, K., Hegde, D., Petrov, S.: Temporal analysis of language through neural language models. In: Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, pp. 61–65 (2014). https://doi.org/10.3115/v1/W14-2517 . http://aclweb.org/anthology/W14-2517 Hamilton et al. [2016a] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Diachronic word embeddings reveal statistical laws of semantic change. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 1489–1501 (2016). https://doi.org/10.18653/v1/P16-1141 . http://aclweb.org/anthology/P16-1141 Hamilton et al. [2016b] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Cultural shift or linguistic drift? comparing two computational measures of semantic change. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2116–2121. Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1229 . https://www.aclweb.org/anthology/D16-1229 Yin et al. [2018] Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. 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[2013] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Kutuzov et al. [2018] Kutuzov, A., Øvrelid, L., Szymanski, T., Velldal, E.: Diachronic word embeddings and semantic shifts: a survey. In: Bender, E.M., Derczynski, L., Isabelle, P. (eds.) Proceedings of the 27th International Conference on Computational Linguistics, pp. 1384–1397. Association for Computational Linguistics, Santa Fe, New Mexico, USA (2018). https://aclanthology.org/C18-1117 Tang [2018] Tang, X.: A state-of-the-art of semantic change computation. Natural Language Engineering 24(5), 649–676 (2018) Kim et al. [2014] Kim, Y., Chiu, Y.-I., Hanaki, K., Hegde, D., Petrov, S.: Temporal analysis of language through neural language models. In: Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, pp. 61–65 (2014). https://doi.org/10.3115/v1/W14-2517 . http://aclweb.org/anthology/W14-2517 Hamilton et al. [2016a] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Diachronic word embeddings reveal statistical laws of semantic change. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 1489–1501 (2016). https://doi.org/10.18653/v1/P16-1141 . http://aclweb.org/anthology/P16-1141 Hamilton et al. [2016b] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Cultural shift or linguistic drift? comparing two computational measures of semantic change. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2116–2121. Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1229 . https://www.aclweb.org/anthology/D16-1229 Yin et al. 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Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Juola, P.: The time course of language change. Computers and the Humanities 37(1), 77–96 (2003) Hilpert and Gries [2008] Hilpert, M., Gries, S.T.: Assessing frequency changes in multistage diachronic corpora: Applications for historical corpus linguistics and the study of language acquisition. Literary and Linguistic Computing 24(4), 385–401 (2008) Mikolov et al. [2013] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Kutuzov et al. [2018] Kutuzov, A., Øvrelid, L., Szymanski, T., Velldal, E.: Diachronic word embeddings and semantic shifts: a survey. In: Bender, E.M., Derczynski, L., Isabelle, P. (eds.) Proceedings of the 27th International Conference on Computational Linguistics, pp. 1384–1397. Association for Computational Linguistics, Santa Fe, New Mexico, USA (2018). https://aclanthology.org/C18-1117 Tang [2018] Tang, X.: A state-of-the-art of semantic change computation. Natural Language Engineering 24(5), 649–676 (2018) Kim et al. [2014] Kim, Y., Chiu, Y.-I., Hanaki, K., Hegde, D., Petrov, S.: Temporal analysis of language through neural language models. In: Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, pp. 61–65 (2014). https://doi.org/10.3115/v1/W14-2517 . http://aclweb.org/anthology/W14-2517 Hamilton et al. [2016a] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Diachronic word embeddings reveal statistical laws of semantic change. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 1489–1501 (2016). https://doi.org/10.18653/v1/P16-1141 . http://aclweb.org/anthology/P16-1141 Hamilton et al. [2016b] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Cultural shift or linguistic drift? comparing two computational measures of semantic change. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2116–2121. Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1229 . https://www.aclweb.org/anthology/D16-1229 Yin et al. [2018] Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. 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In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. 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[2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. 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[2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. 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In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. 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[2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. 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[2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. 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Curran Associates Inc., Red Hook, NY, USA (2013) Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. 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International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. 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[2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. 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Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. 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[2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. 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Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. 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In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. 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Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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[2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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[2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. 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PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. 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Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. 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[2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. 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Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. 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In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. 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Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. 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In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. 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In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. 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[2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. 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[2013] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Kutuzov et al. [2018] Kutuzov, A., Øvrelid, L., Szymanski, T., Velldal, E.: Diachronic word embeddings and semantic shifts: a survey. In: Bender, E.M., Derczynski, L., Isabelle, P. (eds.) Proceedings of the 27th International Conference on Computational Linguistics, pp. 1384–1397. Association for Computational Linguistics, Santa Fe, New Mexico, USA (2018). https://aclanthology.org/C18-1117 Tang [2018] Tang, X.: A state-of-the-art of semantic change computation. Natural Language Engineering 24(5), 649–676 (2018) Kim et al. [2014] Kim, Y., Chiu, Y.-I., Hanaki, K., Hegde, D., Petrov, S.: Temporal analysis of language through neural language models. In: Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, pp. 61–65 (2014). https://doi.org/10.3115/v1/W14-2517 . http://aclweb.org/anthology/W14-2517 Hamilton et al. [2016a] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Diachronic word embeddings reveal statistical laws of semantic change. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 1489–1501 (2016). https://doi.org/10.18653/v1/P16-1141 . http://aclweb.org/anthology/P16-1141 Hamilton et al. [2016b] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Cultural shift or linguistic drift? comparing two computational measures of semantic change. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2116–2121. Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1229 . https://www.aclweb.org/anthology/D16-1229 Yin et al. [2018] Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. 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In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. 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PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Hilpert, M., Gries, S.T.: Assessing frequency changes in multistage diachronic corpora: Applications for historical corpus linguistics and the study of language acquisition. Literary and Linguistic Computing 24(4), 385–401 (2008) Mikolov et al. [2013] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Kutuzov et al. [2018] Kutuzov, A., Øvrelid, L., Szymanski, T., Velldal, E.: Diachronic word embeddings and semantic shifts: a survey. In: Bender, E.M., Derczynski, L., Isabelle, P. (eds.) Proceedings of the 27th International Conference on Computational Linguistics, pp. 1384–1397. Association for Computational Linguistics, Santa Fe, New Mexico, USA (2018). https://aclanthology.org/C18-1117 Tang [2018] Tang, X.: A state-of-the-art of semantic change computation. Natural Language Engineering 24(5), 649–676 (2018) Kim et al. [2014] Kim, Y., Chiu, Y.-I., Hanaki, K., Hegde, D., Petrov, S.: Temporal analysis of language through neural language models. In: Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, pp. 61–65 (2014). https://doi.org/10.3115/v1/W14-2517 . http://aclweb.org/anthology/W14-2517 Hamilton et al. [2016a] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Diachronic word embeddings reveal statistical laws of semantic change. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 1489–1501 (2016). https://doi.org/10.18653/v1/P16-1141 . http://aclweb.org/anthology/P16-1141 Hamilton et al. [2016b] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Cultural shift or linguistic drift? comparing two computational measures of semantic change. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2116–2121. Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1229 . https://www.aclweb.org/anthology/D16-1229 Yin et al. [2018] Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. 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In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. 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[2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. 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In: Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, pp. 61–65 (2014). https://doi.org/10.3115/v1/W14-2517 . http://aclweb.org/anthology/W14-2517 Hamilton et al. [2016a] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Diachronic word embeddings reveal statistical laws of semantic change. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 1489–1501 (2016). https://doi.org/10.18653/v1/P16-1141 . http://aclweb.org/anthology/P16-1141 Hamilton et al. [2016b] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Cultural shift or linguistic drift? comparing two computational measures of semantic change. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2116–2121. Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1229 . https://www.aclweb.org/anthology/D16-1229 Yin et al. [2018] Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. 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International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. 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[2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. 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[2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. 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In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. 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In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. 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Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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[2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. 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[2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. 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In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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[2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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[2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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[2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. 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In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. 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International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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[2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. 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Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Kutuzov, A., Pivovarova, L.: Three-part diachronic semantic change dataset for Russian. In: Proceedings of the 2nd International Workshop on Computational Approaches to Historical Language Change 2021, pp. 7–13. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.lchange-1.2 . https://aclanthology.org/2021.lchange-1.2 Rodina and Kutuzov [2020] Rodina, J., Kutuzov, A.: RuSemShift: a dataset of historical lexical semantic change in Russian. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 1037–1047. International Committee on Computational Linguistics, Barcelona, Spain (Online) (2020). https://doi.org/10.18653/v1/2020.coling-main.90 . https://aclanthology.org/2020.coling-main.90 Martinc et al. [2022] Martinc, M., Dobrovoljc, K., Pollak, S.: Semantic change detection datasets for Slovenian 1.0. Slovenian language resource repository CLARIN.SI (2022). http://hdl.handle.net/11356/1651 Juola [2003] Juola, P.: The time course of language change. Computers and the Humanities 37(1), 77–96 (2003) Hilpert and Gries [2008] Hilpert, M., Gries, S.T.: Assessing frequency changes in multistage diachronic corpora: Applications for historical corpus linguistics and the study of language acquisition. Literary and Linguistic Computing 24(4), 385–401 (2008) Mikolov et al. [2013] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Kutuzov et al. [2018] Kutuzov, A., Øvrelid, L., Szymanski, T., Velldal, E.: Diachronic word embeddings and semantic shifts: a survey. In: Bender, E.M., Derczynski, L., Isabelle, P. (eds.) Proceedings of the 27th International Conference on Computational Linguistics, pp. 1384–1397. Association for Computational Linguistics, Santa Fe, New Mexico, USA (2018). https://aclanthology.org/C18-1117 Tang [2018] Tang, X.: A state-of-the-art of semantic change computation. Natural Language Engineering 24(5), 649–676 (2018) Kim et al. [2014] Kim, Y., Chiu, Y.-I., Hanaki, K., Hegde, D., Petrov, S.: Temporal analysis of language through neural language models. In: Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, pp. 61–65 (2014). https://doi.org/10.3115/v1/W14-2517 . http://aclweb.org/anthology/W14-2517 Hamilton et al. [2016a] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Diachronic word embeddings reveal statistical laws of semantic change. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 1489–1501 (2016). https://doi.org/10.18653/v1/P16-1141 . http://aclweb.org/anthology/P16-1141 Hamilton et al. [2016b] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Cultural shift or linguistic drift? comparing two computational measures of semantic change. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2116–2121. Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1229 . https://www.aclweb.org/anthology/D16-1229 Yin et al. [2018] Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Rodina, J., Kutuzov, A.: RuSemShift: a dataset of historical lexical semantic change in Russian. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 1037–1047. International Committee on Computational Linguistics, Barcelona, Spain (Online) (2020). https://doi.org/10.18653/v1/2020.coling-main.90 . https://aclanthology.org/2020.coling-main.90 Martinc et al. [2022] Martinc, M., Dobrovoljc, K., Pollak, S.: Semantic change detection datasets for Slovenian 1.0. Slovenian language resource repository CLARIN.SI (2022). http://hdl.handle.net/11356/1651 Juola [2003] Juola, P.: The time course of language change. Computers and the Humanities 37(1), 77–96 (2003) Hilpert and Gries [2008] Hilpert, M., Gries, S.T.: Assessing frequency changes in multistage diachronic corpora: Applications for historical corpus linguistics and the study of language acquisition. Literary and Linguistic Computing 24(4), 385–401 (2008) Mikolov et al. [2013] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Kutuzov et al. [2018] Kutuzov, A., Øvrelid, L., Szymanski, T., Velldal, E.: Diachronic word embeddings and semantic shifts: a survey. In: Bender, E.M., Derczynski, L., Isabelle, P. (eds.) Proceedings of the 27th International Conference on Computational Linguistics, pp. 1384–1397. Association for Computational Linguistics, Santa Fe, New Mexico, USA (2018). https://aclanthology.org/C18-1117 Tang [2018] Tang, X.: A state-of-the-art of semantic change computation. Natural Language Engineering 24(5), 649–676 (2018) Kim et al. [2014] Kim, Y., Chiu, Y.-I., Hanaki, K., Hegde, D., Petrov, S.: Temporal analysis of language through neural language models. In: Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, pp. 61–65 (2014). https://doi.org/10.3115/v1/W14-2517 . http://aclweb.org/anthology/W14-2517 Hamilton et al. [2016a] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Diachronic word embeddings reveal statistical laws of semantic change. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 1489–1501 (2016). https://doi.org/10.18653/v1/P16-1141 . http://aclweb.org/anthology/P16-1141 Hamilton et al. [2016b] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Cultural shift or linguistic drift? comparing two computational measures of semantic change. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2116–2121. Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1229 . https://www.aclweb.org/anthology/D16-1229 Yin et al. [2018] Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. 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[2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. 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[2013] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Kutuzov et al. [2018] Kutuzov, A., Øvrelid, L., Szymanski, T., Velldal, E.: Diachronic word embeddings and semantic shifts: a survey. In: Bender, E.M., Derczynski, L., Isabelle, P. (eds.) Proceedings of the 27th International Conference on Computational Linguistics, pp. 1384–1397. Association for Computational Linguistics, Santa Fe, New Mexico, USA (2018). https://aclanthology.org/C18-1117 Tang [2018] Tang, X.: A state-of-the-art of semantic change computation. Natural Language Engineering 24(5), 649–676 (2018) Kim et al. [2014] Kim, Y., Chiu, Y.-I., Hanaki, K., Hegde, D., Petrov, S.: Temporal analysis of language through neural language models. In: Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, pp. 61–65 (2014). https://doi.org/10.3115/v1/W14-2517 . http://aclweb.org/anthology/W14-2517 Hamilton et al. [2016a] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Diachronic word embeddings reveal statistical laws of semantic change. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 1489–1501 (2016). https://doi.org/10.18653/v1/P16-1141 . http://aclweb.org/anthology/P16-1141 Hamilton et al. [2016b] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Cultural shift or linguistic drift? comparing two computational measures of semantic change. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2116–2121. Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1229 . https://www.aclweb.org/anthology/D16-1229 Yin et al. [2018] Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Juola, P.: The time course of language change. Computers and the Humanities 37(1), 77–96 (2003) Hilpert and Gries [2008] Hilpert, M., Gries, S.T.: Assessing frequency changes in multistage diachronic corpora: Applications for historical corpus linguistics and the study of language acquisition. Literary and Linguistic Computing 24(4), 385–401 (2008) Mikolov et al. [2013] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Kutuzov et al. [2018] Kutuzov, A., Øvrelid, L., Szymanski, T., Velldal, E.: Diachronic word embeddings and semantic shifts: a survey. In: Bender, E.M., Derczynski, L., Isabelle, P. (eds.) Proceedings of the 27th International Conference on Computational Linguistics, pp. 1384–1397. Association for Computational Linguistics, Santa Fe, New Mexico, USA (2018). https://aclanthology.org/C18-1117 Tang [2018] Tang, X.: A state-of-the-art of semantic change computation. Natural Language Engineering 24(5), 649–676 (2018) Kim et al. [2014] Kim, Y., Chiu, Y.-I., Hanaki, K., Hegde, D., Petrov, S.: Temporal analysis of language through neural language models. In: Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, pp. 61–65 (2014). https://doi.org/10.3115/v1/W14-2517 . http://aclweb.org/anthology/W14-2517 Hamilton et al. [2016a] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Diachronic word embeddings reveal statistical laws of semantic change. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 1489–1501 (2016). https://doi.org/10.18653/v1/P16-1141 . http://aclweb.org/anthology/P16-1141 Hamilton et al. [2016b] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Cultural shift or linguistic drift? comparing two computational measures of semantic change. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2116–2121. Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1229 . https://www.aclweb.org/anthology/D16-1229 Yin et al. [2018] Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. 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International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. 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Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. 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International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Kutuzov et al. [2018] Kutuzov, A., Øvrelid, L., Szymanski, T., Velldal, E.: Diachronic word embeddings and semantic shifts: a survey. In: Bender, E.M., Derczynski, L., Isabelle, P. (eds.) Proceedings of the 27th International Conference on Computational Linguistics, pp. 1384–1397. Association for Computational Linguistics, Santa Fe, New Mexico, USA (2018). https://aclanthology.org/C18-1117 Tang [2018] Tang, X.: A state-of-the-art of semantic change computation. Natural Language Engineering 24(5), 649–676 (2018) Kim et al. 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International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. 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[2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Tang, X.: A state-of-the-art of semantic change computation. Natural Language Engineering 24(5), 649–676 (2018) Kim et al. [2014] Kim, Y., Chiu, Y.-I., Hanaki, K., Hegde, D., Petrov, S.: Temporal analysis of language through neural language models. In: Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, pp. 61–65 (2014). https://doi.org/10.3115/v1/W14-2517 . http://aclweb.org/anthology/W14-2517 Hamilton et al. [2016a] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Diachronic word embeddings reveal statistical laws of semantic change. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 1489–1501 (2016). https://doi.org/10.18653/v1/P16-1141 . http://aclweb.org/anthology/P16-1141 Hamilton et al. [2016b] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Cultural shift or linguistic drift? comparing two computational measures of semantic change. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2116–2121. Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1229 . https://www.aclweb.org/anthology/D16-1229 Yin et al. [2018] Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. 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In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. 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[2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. 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In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. 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Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. 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Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. 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Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. 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In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. 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Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. 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[2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. 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In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. 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In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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[2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. 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International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. 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Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. 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Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. 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[2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Kutuzov, A., Pivovarova, L.: Three-part diachronic semantic change dataset for Russian. In: Proceedings of the 2nd International Workshop on Computational Approaches to Historical Language Change 2021, pp. 7–13. 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[2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Rodina, J., Kutuzov, A.: RuSemShift: a dataset of historical lexical semantic change in Russian. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 1037–1047. International Committee on Computational Linguistics, Barcelona, Spain (Online) (2020). https://doi.org/10.18653/v1/2020.coling-main.90 . https://aclanthology.org/2020.coling-main.90 Martinc et al. [2022] Martinc, M., Dobrovoljc, K., Pollak, S.: Semantic change detection datasets for Slovenian 1.0. Slovenian language resource repository CLARIN.SI (2022). http://hdl.handle.net/11356/1651 Juola [2003] Juola, P.: The time course of language change. Computers and the Humanities 37(1), 77–96 (2003) Hilpert and Gries [2008] Hilpert, M., Gries, S.T.: Assessing frequency changes in multistage diachronic corpora: Applications for historical corpus linguistics and the study of language acquisition. Literary and Linguistic Computing 24(4), 385–401 (2008) Mikolov et al. [2013] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Kutuzov et al. [2018] Kutuzov, A., Øvrelid, L., Szymanski, T., Velldal, E.: Diachronic word embeddings and semantic shifts: a survey. In: Bender, E.M., Derczynski, L., Isabelle, P. (eds.) Proceedings of the 27th International Conference on Computational Linguistics, pp. 1384–1397. Association for Computational Linguistics, Santa Fe, New Mexico, USA (2018). https://aclanthology.org/C18-1117 Tang [2018] Tang, X.: A state-of-the-art of semantic change computation. Natural Language Engineering 24(5), 649–676 (2018) Kim et al. [2014] Kim, Y., Chiu, Y.-I., Hanaki, K., Hegde, D., Petrov, S.: Temporal analysis of language through neural language models. In: Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, pp. 61–65 (2014). https://doi.org/10.3115/v1/W14-2517 . http://aclweb.org/anthology/W14-2517 Hamilton et al. [2016a] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Diachronic word embeddings reveal statistical laws of semantic change. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 1489–1501 (2016). https://doi.org/10.18653/v1/P16-1141 . http://aclweb.org/anthology/P16-1141 Hamilton et al. [2016b] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Cultural shift or linguistic drift? comparing two computational measures of semantic change. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2116–2121. Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1229 . https://www.aclweb.org/anthology/D16-1229 Yin et al. [2018] Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. 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[2013] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Kutuzov et al. [2018] Kutuzov, A., Øvrelid, L., Szymanski, T., Velldal, E.: Diachronic word embeddings and semantic shifts: a survey. In: Bender, E.M., Derczynski, L., Isabelle, P. (eds.) Proceedings of the 27th International Conference on Computational Linguistics, pp. 1384–1397. Association for Computational Linguistics, Santa Fe, New Mexico, USA (2018). https://aclanthology.org/C18-1117 Tang [2018] Tang, X.: A state-of-the-art of semantic change computation. Natural Language Engineering 24(5), 649–676 (2018) Kim et al. [2014] Kim, Y., Chiu, Y.-I., Hanaki, K., Hegde, D., Petrov, S.: Temporal analysis of language through neural language models. In: Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, pp. 61–65 (2014). https://doi.org/10.3115/v1/W14-2517 . http://aclweb.org/anthology/W14-2517 Hamilton et al. [2016a] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Diachronic word embeddings reveal statistical laws of semantic change. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 1489–1501 (2016). https://doi.org/10.18653/v1/P16-1141 . http://aclweb.org/anthology/P16-1141 Hamilton et al. [2016b] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Cultural shift or linguistic drift? comparing two computational measures of semantic change. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2116–2121. Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1229 . https://www.aclweb.org/anthology/D16-1229 Yin et al. 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Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Juola, P.: The time course of language change. Computers and the Humanities 37(1), 77–96 (2003) Hilpert and Gries [2008] Hilpert, M., Gries, S.T.: Assessing frequency changes in multistage diachronic corpora: Applications for historical corpus linguistics and the study of language acquisition. Literary and Linguistic Computing 24(4), 385–401 (2008) Mikolov et al. [2013] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Kutuzov et al. [2018] Kutuzov, A., Øvrelid, L., Szymanski, T., Velldal, E.: Diachronic word embeddings and semantic shifts: a survey. In: Bender, E.M., Derczynski, L., Isabelle, P. (eds.) Proceedings of the 27th International Conference on Computational Linguistics, pp. 1384–1397. Association for Computational Linguistics, Santa Fe, New Mexico, USA (2018). https://aclanthology.org/C18-1117 Tang [2018] Tang, X.: A state-of-the-art of semantic change computation. Natural Language Engineering 24(5), 649–676 (2018) Kim et al. [2014] Kim, Y., Chiu, Y.-I., Hanaki, K., Hegde, D., Petrov, S.: Temporal analysis of language through neural language models. In: Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, pp. 61–65 (2014). https://doi.org/10.3115/v1/W14-2517 . http://aclweb.org/anthology/W14-2517 Hamilton et al. [2016a] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Diachronic word embeddings reveal statistical laws of semantic change. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 1489–1501 (2016). https://doi.org/10.18653/v1/P16-1141 . http://aclweb.org/anthology/P16-1141 Hamilton et al. [2016b] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Cultural shift or linguistic drift? comparing two computational measures of semantic change. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2116–2121. Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1229 . https://www.aclweb.org/anthology/D16-1229 Yin et al. [2018] Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. 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In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. 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[2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. 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[2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. 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In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. 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[2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. 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[2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. 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Curran Associates Inc., Red Hook, NY, USA (2013) Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. 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International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. 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[2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. 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Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. 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[2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. 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Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. 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In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. 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Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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[2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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[2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. 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PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. 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Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. 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[2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. 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Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. 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In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. 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Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. 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In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. 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Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Kutuzov, A., Pivovarova, L.: Three-part diachronic semantic change dataset for Russian. In: Proceedings of the 2nd International Workshop on Computational Approaches to Historical Language Change 2021, pp. 7–13. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.lchange-1.2 . https://aclanthology.org/2021.lchange-1.2 Rodina and Kutuzov [2020] Rodina, J., Kutuzov, A.: RuSemShift: a dataset of historical lexical semantic change in Russian. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 1037–1047. International Committee on Computational Linguistics, Barcelona, Spain (Online) (2020). https://doi.org/10.18653/v1/2020.coling-main.90 . https://aclanthology.org/2020.coling-main.90 Martinc et al. [2022] Martinc, M., Dobrovoljc, K., Pollak, S.: Semantic change detection datasets for Slovenian 1.0. Slovenian language resource repository CLARIN.SI (2022). http://hdl.handle.net/11356/1651 Juola [2003] Juola, P.: The time course of language change. Computers and the Humanities 37(1), 77–96 (2003) Hilpert and Gries [2008] Hilpert, M., Gries, S.T.: Assessing frequency changes in multistage diachronic corpora: Applications for historical corpus linguistics and the study of language acquisition. Literary and Linguistic Computing 24(4), 385–401 (2008) Mikolov et al. [2013] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Kutuzov et al. [2018] Kutuzov, A., Øvrelid, L., Szymanski, T., Velldal, E.: Diachronic word embeddings and semantic shifts: a survey. In: Bender, E.M., Derczynski, L., Isabelle, P. (eds.) Proceedings of the 27th International Conference on Computational Linguistics, pp. 1384–1397. Association for Computational Linguistics, Santa Fe, New Mexico, USA (2018). https://aclanthology.org/C18-1117 Tang [2018] Tang, X.: A state-of-the-art of semantic change computation. Natural Language Engineering 24(5), 649–676 (2018) Kim et al. [2014] Kim, Y., Chiu, Y.-I., Hanaki, K., Hegde, D., Petrov, S.: Temporal analysis of language through neural language models. In: Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, pp. 61–65 (2014). https://doi.org/10.3115/v1/W14-2517 . http://aclweb.org/anthology/W14-2517 Hamilton et al. [2016a] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Diachronic word embeddings reveal statistical laws of semantic change. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 1489–1501 (2016). https://doi.org/10.18653/v1/P16-1141 . http://aclweb.org/anthology/P16-1141 Hamilton et al. [2016b] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Cultural shift or linguistic drift? comparing two computational measures of semantic change. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2116–2121. Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1229 . https://www.aclweb.org/anthology/D16-1229 Yin et al. [2018] Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Rodina, J., Kutuzov, A.: RuSemShift: a dataset of historical lexical semantic change in Russian. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 1037–1047. International Committee on Computational Linguistics, Barcelona, Spain (Online) (2020). https://doi.org/10.18653/v1/2020.coling-main.90 . https://aclanthology.org/2020.coling-main.90 Martinc et al. [2022] Martinc, M., Dobrovoljc, K., Pollak, S.: Semantic change detection datasets for Slovenian 1.0. Slovenian language resource repository CLARIN.SI (2022). http://hdl.handle.net/11356/1651 Juola [2003] Juola, P.: The time course of language change. Computers and the Humanities 37(1), 77–96 (2003) Hilpert and Gries [2008] Hilpert, M., Gries, S.T.: Assessing frequency changes in multistage diachronic corpora: Applications for historical corpus linguistics and the study of language acquisition. Literary and Linguistic Computing 24(4), 385–401 (2008) Mikolov et al. [2013] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Kutuzov et al. [2018] Kutuzov, A., Øvrelid, L., Szymanski, T., Velldal, E.: Diachronic word embeddings and semantic shifts: a survey. In: Bender, E.M., Derczynski, L., Isabelle, P. (eds.) Proceedings of the 27th International Conference on Computational Linguistics, pp. 1384–1397. Association for Computational Linguistics, Santa Fe, New Mexico, USA (2018). https://aclanthology.org/C18-1117 Tang [2018] Tang, X.: A state-of-the-art of semantic change computation. Natural Language Engineering 24(5), 649–676 (2018) Kim et al. [2014] Kim, Y., Chiu, Y.-I., Hanaki, K., Hegde, D., Petrov, S.: Temporal analysis of language through neural language models. In: Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, pp. 61–65 (2014). https://doi.org/10.3115/v1/W14-2517 . http://aclweb.org/anthology/W14-2517 Hamilton et al. [2016a] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Diachronic word embeddings reveal statistical laws of semantic change. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 1489–1501 (2016). https://doi.org/10.18653/v1/P16-1141 . http://aclweb.org/anthology/P16-1141 Hamilton et al. [2016b] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Cultural shift or linguistic drift? comparing two computational measures of semantic change. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2116–2121. Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1229 . https://www.aclweb.org/anthology/D16-1229 Yin et al. [2018] Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. 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[2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. 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[2013] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Kutuzov et al. [2018] Kutuzov, A., Øvrelid, L., Szymanski, T., Velldal, E.: Diachronic word embeddings and semantic shifts: a survey. In: Bender, E.M., Derczynski, L., Isabelle, P. (eds.) Proceedings of the 27th International Conference on Computational Linguistics, pp. 1384–1397. Association for Computational Linguistics, Santa Fe, New Mexico, USA (2018). https://aclanthology.org/C18-1117 Tang [2018] Tang, X.: A state-of-the-art of semantic change computation. Natural Language Engineering 24(5), 649–676 (2018) Kim et al. [2014] Kim, Y., Chiu, Y.-I., Hanaki, K., Hegde, D., Petrov, S.: Temporal analysis of language through neural language models. In: Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, pp. 61–65 (2014). https://doi.org/10.3115/v1/W14-2517 . http://aclweb.org/anthology/W14-2517 Hamilton et al. [2016a] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Diachronic word embeddings reveal statistical laws of semantic change. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 1489–1501 (2016). https://doi.org/10.18653/v1/P16-1141 . http://aclweb.org/anthology/P16-1141 Hamilton et al. [2016b] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Cultural shift or linguistic drift? comparing two computational measures of semantic change. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2116–2121. Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1229 . https://www.aclweb.org/anthology/D16-1229 Yin et al. [2018] Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Juola, P.: The time course of language change. Computers and the Humanities 37(1), 77–96 (2003) Hilpert and Gries [2008] Hilpert, M., Gries, S.T.: Assessing frequency changes in multistage diachronic corpora: Applications for historical corpus linguistics and the study of language acquisition. Literary and Linguistic Computing 24(4), 385–401 (2008) Mikolov et al. [2013] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Kutuzov et al. [2018] Kutuzov, A., Øvrelid, L., Szymanski, T., Velldal, E.: Diachronic word embeddings and semantic shifts: a survey. In: Bender, E.M., Derczynski, L., Isabelle, P. (eds.) Proceedings of the 27th International Conference on Computational Linguistics, pp. 1384–1397. Association for Computational Linguistics, Santa Fe, New Mexico, USA (2018). https://aclanthology.org/C18-1117 Tang [2018] Tang, X.: A state-of-the-art of semantic change computation. Natural Language Engineering 24(5), 649–676 (2018) Kim et al. [2014] Kim, Y., Chiu, Y.-I., Hanaki, K., Hegde, D., Petrov, S.: Temporal analysis of language through neural language models. In: Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, pp. 61–65 (2014). https://doi.org/10.3115/v1/W14-2517 . http://aclweb.org/anthology/W14-2517 Hamilton et al. [2016a] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Diachronic word embeddings reveal statistical laws of semantic change. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 1489–1501 (2016). https://doi.org/10.18653/v1/P16-1141 . http://aclweb.org/anthology/P16-1141 Hamilton et al. [2016b] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Cultural shift or linguistic drift? comparing two computational measures of semantic change. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2116–2121. Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1229 . https://www.aclweb.org/anthology/D16-1229 Yin et al. [2018] Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. 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International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. 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Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. 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International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Kutuzov et al. [2018] Kutuzov, A., Øvrelid, L., Szymanski, T., Velldal, E.: Diachronic word embeddings and semantic shifts: a survey. In: Bender, E.M., Derczynski, L., Isabelle, P. (eds.) Proceedings of the 27th International Conference on Computational Linguistics, pp. 1384–1397. Association for Computational Linguistics, Santa Fe, New Mexico, USA (2018). https://aclanthology.org/C18-1117 Tang [2018] Tang, X.: A state-of-the-art of semantic change computation. Natural Language Engineering 24(5), 649–676 (2018) Kim et al. 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International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. 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[2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Tang, X.: A state-of-the-art of semantic change computation. Natural Language Engineering 24(5), 649–676 (2018) Kim et al. [2014] Kim, Y., Chiu, Y.-I., Hanaki, K., Hegde, D., Petrov, S.: Temporal analysis of language through neural language models. In: Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, pp. 61–65 (2014). https://doi.org/10.3115/v1/W14-2517 . http://aclweb.org/anthology/W14-2517 Hamilton et al. [2016a] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Diachronic word embeddings reveal statistical laws of semantic change. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 1489–1501 (2016). https://doi.org/10.18653/v1/P16-1141 . http://aclweb.org/anthology/P16-1141 Hamilton et al. [2016b] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Cultural shift or linguistic drift? comparing two computational measures of semantic change. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2116–2121. Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1229 . https://www.aclweb.org/anthology/D16-1229 Yin et al. [2018] Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. 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In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. 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[2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. 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In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. 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Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. 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Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. 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Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. 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In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. 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Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. 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[2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. 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In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. 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In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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[2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. 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International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. 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Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. 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Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.lchange-1.2 . https://aclanthology.org/2021.lchange-1.2 Rodina and Kutuzov [2020] Rodina, J., Kutuzov, A.: RuSemShift: a dataset of historical lexical semantic change in Russian. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 1037–1047. International Committee on Computational Linguistics, Barcelona, Spain (Online) (2020). https://doi.org/10.18653/v1/2020.coling-main.90 . https://aclanthology.org/2020.coling-main.90 Martinc et al. [2022] Martinc, M., Dobrovoljc, K., Pollak, S.: Semantic change detection datasets for Slovenian 1.0. Slovenian language resource repository CLARIN.SI (2022). http://hdl.handle.net/11356/1651 Juola [2003] Juola, P.: The time course of language change. 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In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. 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[2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Martinc, M., Dobrovoljc, K., Pollak, S.: Semantic change detection datasets for Slovenian 1.0. Slovenian language resource repository CLARIN.SI (2022). http://hdl.handle.net/11356/1651 Juola [2003] Juola, P.: The time course of language change. Computers and the Humanities 37(1), 77–96 (2003) Hilpert and Gries [2008] Hilpert, M., Gries, S.T.: Assessing frequency changes in multistage diachronic corpora: Applications for historical corpus linguistics and the study of language acquisition. Literary and Linguistic Computing 24(4), 385–401 (2008) Mikolov et al. [2013] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Kutuzov et al. [2018] Kutuzov, A., Øvrelid, L., Szymanski, T., Velldal, E.: Diachronic word embeddings and semantic shifts: a survey. In: Bender, E.M., Derczynski, L., Isabelle, P. (eds.) Proceedings of the 27th International Conference on Computational Linguistics, pp. 1384–1397. Association for Computational Linguistics, Santa Fe, New Mexico, USA (2018). https://aclanthology.org/C18-1117 Tang [2018] Tang, X.: A state-of-the-art of semantic change computation. Natural Language Engineering 24(5), 649–676 (2018) Kim et al. [2014] Kim, Y., Chiu, Y.-I., Hanaki, K., Hegde, D., Petrov, S.: Temporal analysis of language through neural language models. In: Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, pp. 61–65 (2014). https://doi.org/10.3115/v1/W14-2517 . http://aclweb.org/anthology/W14-2517 Hamilton et al. [2016a] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Diachronic word embeddings reveal statistical laws of semantic change. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 1489–1501 (2016). https://doi.org/10.18653/v1/P16-1141 . http://aclweb.org/anthology/P16-1141 Hamilton et al. [2016b] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Cultural shift or linguistic drift? comparing two computational measures of semantic change. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2116–2121. Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1229 . https://www.aclweb.org/anthology/D16-1229 Yin et al. [2018] Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. 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In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. 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Proceedings of the 27th International Conference on Computational Linguistics, pp. 1384–1397. Association for Computational Linguistics, Santa Fe, New Mexico, USA (2018). https://aclanthology.org/C18-1117 Tang [2018] Tang, X.: A state-of-the-art of semantic change computation. Natural Language Engineering 24(5), 649–676 (2018) Kim et al. [2014] Kim, Y., Chiu, Y.-I., Hanaki, K., Hegde, D., Petrov, S.: Temporal analysis of language through neural language models. In: Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, pp. 61–65 (2014). https://doi.org/10.3115/v1/W14-2517 . http://aclweb.org/anthology/W14-2517 Hamilton et al. [2016a] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Diachronic word embeddings reveal statistical laws of semantic change. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 1489–1501 (2016). https://doi.org/10.18653/v1/P16-1141 . http://aclweb.org/anthology/P16-1141 Hamilton et al. [2016b] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Cultural shift or linguistic drift? comparing two computational measures of semantic change. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2116–2121. Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1229 . https://www.aclweb.org/anthology/D16-1229 Yin et al. [2018] Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Hilpert, M., Gries, S.T.: Assessing frequency changes in multistage diachronic corpora: Applications for historical corpus linguistics and the study of language acquisition. Literary and Linguistic Computing 24(4), 385–401 (2008) Mikolov et al. [2013] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Kutuzov et al. [2018] Kutuzov, A., Øvrelid, L., Szymanski, T., Velldal, E.: Diachronic word embeddings and semantic shifts: a survey. In: Bender, E.M., Derczynski, L., Isabelle, P. (eds.) Proceedings of the 27th International Conference on Computational Linguistics, pp. 1384–1397. Association for Computational Linguistics, Santa Fe, New Mexico, USA (2018). https://aclanthology.org/C18-1117 Tang [2018] Tang, X.: A state-of-the-art of semantic change computation. Natural Language Engineering 24(5), 649–676 (2018) Kim et al. [2014] Kim, Y., Chiu, Y.-I., Hanaki, K., Hegde, D., Petrov, S.: Temporal analysis of language through neural language models. In: Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, pp. 61–65 (2014). https://doi.org/10.3115/v1/W14-2517 . http://aclweb.org/anthology/W14-2517 Hamilton et al. [2016a] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Diachronic word embeddings reveal statistical laws of semantic change. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 1489–1501 (2016). https://doi.org/10.18653/v1/P16-1141 . http://aclweb.org/anthology/P16-1141 Hamilton et al. [2016b] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Cultural shift or linguistic drift? comparing two computational measures of semantic change. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2116–2121. Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1229 . https://www.aclweb.org/anthology/D16-1229 Yin et al. [2018] Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. 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Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. 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In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Kutuzov et al. [2018] Kutuzov, A., Øvrelid, L., Szymanski, T., Velldal, E.: Diachronic word embeddings and semantic shifts: a survey. In: Bender, E.M., Derczynski, L., Isabelle, P. (eds.) Proceedings of the 27th International Conference on Computational Linguistics, pp. 1384–1397. Association for Computational Linguistics, Santa Fe, New Mexico, USA (2018). https://aclanthology.org/C18-1117 Tang [2018] Tang, X.: A state-of-the-art of semantic change computation. Natural Language Engineering 24(5), 649–676 (2018) Kim et al. [2014] Kim, Y., Chiu, Y.-I., Hanaki, K., Hegde, D., Petrov, S.: Temporal analysis of language through neural language models. In: Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, pp. 61–65 (2014). https://doi.org/10.3115/v1/W14-2517 . http://aclweb.org/anthology/W14-2517 Hamilton et al. [2016a] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Diachronic word embeddings reveal statistical laws of semantic change. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 1489–1501 (2016). https://doi.org/10.18653/v1/P16-1141 . http://aclweb.org/anthology/P16-1141 Hamilton et al. [2016b] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Cultural shift or linguistic drift? comparing two computational measures of semantic change. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2116–2121. Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1229 . https://www.aclweb.org/anthology/D16-1229 Yin et al. [2018] Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Kutuzov, A., Øvrelid, L., Szymanski, T., Velldal, E.: Diachronic word embeddings and semantic shifts: a survey. In: Bender, E.M., Derczynski, L., Isabelle, P. (eds.) Proceedings of the 27th International Conference on Computational Linguistics, pp. 1384–1397. Association for Computational Linguistics, Santa Fe, New Mexico, USA (2018). https://aclanthology.org/C18-1117 Tang [2018] Tang, X.: A state-of-the-art of semantic change computation. Natural Language Engineering 24(5), 649–676 (2018) Kim et al. [2014] Kim, Y., Chiu, Y.-I., Hanaki, K., Hegde, D., Petrov, S.: Temporal analysis of language through neural language models. In: Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, pp. 61–65 (2014). https://doi.org/10.3115/v1/W14-2517 . http://aclweb.org/anthology/W14-2517 Hamilton et al. [2016a] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Diachronic word embeddings reveal statistical laws of semantic change. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 1489–1501 (2016). https://doi.org/10.18653/v1/P16-1141 . http://aclweb.org/anthology/P16-1141 Hamilton et al. [2016b] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Cultural shift or linguistic drift? comparing two computational measures of semantic change. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2116–2121. Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1229 . https://www.aclweb.org/anthology/D16-1229 Yin et al. [2018] Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. 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[2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. 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Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Kim, Y., Chiu, Y.-I., Hanaki, K., Hegde, D., Petrov, S.: Temporal analysis of language through neural language models. In: Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, pp. 61–65 (2014). https://doi.org/10.3115/v1/W14-2517 . http://aclweb.org/anthology/W14-2517 Hamilton et al. [2016a] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Diachronic word embeddings reveal statistical laws of semantic change. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 1489–1501 (2016). https://doi.org/10.18653/v1/P16-1141 . http://aclweb.org/anthology/P16-1141 Hamilton et al. [2016b] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Cultural shift or linguistic drift? comparing two computational measures of semantic change. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2116–2121. Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1229 . https://www.aclweb.org/anthology/D16-1229 Yin et al. [2018] Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. 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[2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. 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Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. 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Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. 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[2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. 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[2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. 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IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. 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Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. 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PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. 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[2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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[2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. 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Curran Associates Inc., Red Hook, NY, USA (2013) Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. 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In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. 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Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. 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[2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. 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Association for Computational Linguistics, New Orleans, Louisiana (2018). https://doi.org/10.18653/v1/N18-2027 . https://www.aclweb.org/anthology/N18-2027 Kutuzov and Pivovarova [2021] Kutuzov, A., Pivovarova, L.: Three-part diachronic semantic change dataset for Russian. In: Proceedings of the 2nd International Workshop on Computational Approaches to Historical Language Change 2021, pp. 7–13. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.lchange-1.2 . https://aclanthology.org/2021.lchange-1.2 Rodina and Kutuzov [2020] Rodina, J., Kutuzov, A.: RuSemShift: a dataset of historical lexical semantic change in Russian. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 1037–1047. International Committee on Computational Linguistics, Barcelona, Spain (Online) (2020). https://doi.org/10.18653/v1/2020.coling-main.90 . https://aclanthology.org/2020.coling-main.90 Martinc et al. [2022] Martinc, M., Dobrovoljc, K., Pollak, S.: Semantic change detection datasets for Slovenian 1.0. Slovenian language resource repository CLARIN.SI (2022). http://hdl.handle.net/11356/1651 Juola [2003] Juola, P.: The time course of language change. Computers and the Humanities 37(1), 77–96 (2003) Hilpert and Gries [2008] Hilpert, M., Gries, S.T.: Assessing frequency changes in multistage diachronic corpora: Applications for historical corpus linguistics and the study of language acquisition. Literary and Linguistic Computing 24(4), 385–401 (2008) Mikolov et al. [2013] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Kutuzov et al. [2018] Kutuzov, A., Øvrelid, L., Szymanski, T., Velldal, E.: Diachronic word embeddings and semantic shifts: a survey. In: Bender, E.M., Derczynski, L., Isabelle, P. (eds.) Proceedings of the 27th International Conference on Computational Linguistics, pp. 1384–1397. Association for Computational Linguistics, Santa Fe, New Mexico, USA (2018). https://aclanthology.org/C18-1117 Tang [2018] Tang, X.: A state-of-the-art of semantic change computation. Natural Language Engineering 24(5), 649–676 (2018) Kim et al. [2014] Kim, Y., Chiu, Y.-I., Hanaki, K., Hegde, D., Petrov, S.: Temporal analysis of language through neural language models. In: Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, pp. 61–65 (2014). https://doi.org/10.3115/v1/W14-2517 . http://aclweb.org/anthology/W14-2517 Hamilton et al. [2016a] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Diachronic word embeddings reveal statistical laws of semantic change. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 1489–1501 (2016). https://doi.org/10.18653/v1/P16-1141 . http://aclweb.org/anthology/P16-1141 Hamilton et al. [2016b] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Cultural shift or linguistic drift? comparing two computational measures of semantic change. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2116–2121. Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1229 . https://www.aclweb.org/anthology/D16-1229 Yin et al. [2018] Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Kutuzov, A., Pivovarova, L.: Three-part diachronic semantic change dataset for Russian. In: Proceedings of the 2nd International Workshop on Computational Approaches to Historical Language Change 2021, pp. 7–13. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.lchange-1.2 . https://aclanthology.org/2021.lchange-1.2 Rodina and Kutuzov [2020] Rodina, J., Kutuzov, A.: RuSemShift: a dataset of historical lexical semantic change in Russian. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 1037–1047. International Committee on Computational Linguistics, Barcelona, Spain (Online) (2020). https://doi.org/10.18653/v1/2020.coling-main.90 . https://aclanthology.org/2020.coling-main.90 Martinc et al. [2022] Martinc, M., Dobrovoljc, K., Pollak, S.: Semantic change detection datasets for Slovenian 1.0. Slovenian language resource repository CLARIN.SI (2022). http://hdl.handle.net/11356/1651 Juola [2003] Juola, P.: The time course of language change. Computers and the Humanities 37(1), 77–96 (2003) Hilpert and Gries [2008] Hilpert, M., Gries, S.T.: Assessing frequency changes in multistage diachronic corpora: Applications for historical corpus linguistics and the study of language acquisition. Literary and Linguistic Computing 24(4), 385–401 (2008) Mikolov et al. [2013] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Kutuzov et al. [2018] Kutuzov, A., Øvrelid, L., Szymanski, T., Velldal, E.: Diachronic word embeddings and semantic shifts: a survey. In: Bender, E.M., Derczynski, L., Isabelle, P. (eds.) Proceedings of the 27th International Conference on Computational Linguistics, pp. 1384–1397. Association for Computational Linguistics, Santa Fe, New Mexico, USA (2018). https://aclanthology.org/C18-1117 Tang [2018] Tang, X.: A state-of-the-art of semantic change computation. Natural Language Engineering 24(5), 649–676 (2018) Kim et al. [2014] Kim, Y., Chiu, Y.-I., Hanaki, K., Hegde, D., Petrov, S.: Temporal analysis of language through neural language models. In: Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, pp. 61–65 (2014). https://doi.org/10.3115/v1/W14-2517 . http://aclweb.org/anthology/W14-2517 Hamilton et al. [2016a] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Diachronic word embeddings reveal statistical laws of semantic change. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 1489–1501 (2016). https://doi.org/10.18653/v1/P16-1141 . http://aclweb.org/anthology/P16-1141 Hamilton et al. [2016b] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Cultural shift or linguistic drift? comparing two computational measures of semantic change. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2116–2121. Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1229 . https://www.aclweb.org/anthology/D16-1229 Yin et al. [2018] Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. 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[2013] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Kutuzov et al. [2018] Kutuzov, A., Øvrelid, L., Szymanski, T., Velldal, E.: Diachronic word embeddings and semantic shifts: a survey. In: Bender, E.M., Derczynski, L., Isabelle, P. (eds.) Proceedings of the 27th International Conference on Computational Linguistics, pp. 1384–1397. Association for Computational Linguistics, Santa Fe, New Mexico, USA (2018). https://aclanthology.org/C18-1117 Tang [2018] Tang, X.: A state-of-the-art of semantic change computation. Natural Language Engineering 24(5), 649–676 (2018) Kim et al. [2014] Kim, Y., Chiu, Y.-I., Hanaki, K., Hegde, D., Petrov, S.: Temporal analysis of language through neural language models. In: Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, pp. 61–65 (2014). https://doi.org/10.3115/v1/W14-2517 . http://aclweb.org/anthology/W14-2517 Hamilton et al. [2016a] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Diachronic word embeddings reveal statistical laws of semantic change. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 1489–1501 (2016). https://doi.org/10.18653/v1/P16-1141 . http://aclweb.org/anthology/P16-1141 Hamilton et al. [2016b] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Cultural shift or linguistic drift? comparing two computational measures of semantic change. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2116–2121. Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1229 . https://www.aclweb.org/anthology/D16-1229 Yin et al. 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International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. 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Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. 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In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. 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PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. 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In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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[2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Hilpert, M., Gries, S.T.: Assessing frequency changes in multistage diachronic corpora: Applications for historical corpus linguistics and the study of language acquisition. Literary and Linguistic Computing 24(4), 385–401 (2008) Mikolov et al. [2013] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Kutuzov et al. [2018] Kutuzov, A., Øvrelid, L., Szymanski, T., Velldal, E.: Diachronic word embeddings and semantic shifts: a survey. In: Bender, E.M., Derczynski, L., Isabelle, P. (eds.) Proceedings of the 27th International Conference on Computational Linguistics, pp. 1384–1397. Association for Computational Linguistics, Santa Fe, New Mexico, USA (2018). https://aclanthology.org/C18-1117 Tang [2018] Tang, X.: A state-of-the-art of semantic change computation. Natural Language Engineering 24(5), 649–676 (2018) Kim et al. [2014] Kim, Y., Chiu, Y.-I., Hanaki, K., Hegde, D., Petrov, S.: Temporal analysis of language through neural language models. In: Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, pp. 61–65 (2014). https://doi.org/10.3115/v1/W14-2517 . http://aclweb.org/anthology/W14-2517 Hamilton et al. [2016a] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Diachronic word embeddings reveal statistical laws of semantic change. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 1489–1501 (2016). https://doi.org/10.18653/v1/P16-1141 . http://aclweb.org/anthology/P16-1141 Hamilton et al. [2016b] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Cultural shift or linguistic drift? comparing two computational measures of semantic change. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2116–2121. Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1229 . https://www.aclweb.org/anthology/D16-1229 Yin et al. [2018] Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. 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[2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. 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In: Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, pp. 61–65 (2014). https://doi.org/10.3115/v1/W14-2517 . http://aclweb.org/anthology/W14-2517 Hamilton et al. [2016a] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Diachronic word embeddings reveal statistical laws of semantic change. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 1489–1501 (2016). https://doi.org/10.18653/v1/P16-1141 . http://aclweb.org/anthology/P16-1141 Hamilton et al. [2016b] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Cultural shift or linguistic drift? comparing two computational measures of semantic change. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2116–2121. Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1229 . https://www.aclweb.org/anthology/D16-1229 Yin et al. [2018] Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. 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International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. 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[2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. 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[2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. 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[2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Hamilton, W.L., Leskovec, J., Jurafsky, D.: Cultural shift or linguistic drift? comparing two computational measures of semantic change. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2116–2121. Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1229 . https://www.aclweb.org/anthology/D16-1229 Yin et al. [2018] Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. 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In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. 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International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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[2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) 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In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. 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[2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. 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In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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[2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. 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In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. 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[2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. 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[2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. 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In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. 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Association for Computational Linguistics, New Orleans, Louisiana (2018). https://doi.org/10.18653/v1/N18-2027 . https://www.aclweb.org/anthology/N18-2027 Kutuzov and Pivovarova [2021] Kutuzov, A., Pivovarova, L.: Three-part diachronic semantic change dataset for Russian. In: Proceedings of the 2nd International Workshop on Computational Approaches to Historical Language Change 2021, pp. 7–13. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.lchange-1.2 . https://aclanthology.org/2021.lchange-1.2 Rodina and Kutuzov [2020] Rodina, J., Kutuzov, A.: RuSemShift: a dataset of historical lexical semantic change in Russian. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 1037–1047. International Committee on Computational Linguistics, Barcelona, Spain (Online) (2020). https://doi.org/10.18653/v1/2020.coling-main.90 . https://aclanthology.org/2020.coling-main.90 Martinc et al. [2022] Martinc, M., Dobrovoljc, K., Pollak, S.: Semantic change detection datasets for Slovenian 1.0. Slovenian language resource repository CLARIN.SI (2022). http://hdl.handle.net/11356/1651 Juola [2003] Juola, P.: The time course of language change. Computers and the Humanities 37(1), 77–96 (2003) Hilpert and Gries [2008] Hilpert, M., Gries, S.T.: Assessing frequency changes in multistage diachronic corpora: Applications for historical corpus linguistics and the study of language acquisition. Literary and Linguistic Computing 24(4), 385–401 (2008) Mikolov et al. [2013] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Kutuzov et al. [2018] Kutuzov, A., Øvrelid, L., Szymanski, T., Velldal, E.: Diachronic word embeddings and semantic shifts: a survey. In: Bender, E.M., Derczynski, L., Isabelle, P. (eds.) Proceedings of the 27th International Conference on Computational Linguistics, pp. 1384–1397. Association for Computational Linguistics, Santa Fe, New Mexico, USA (2018). https://aclanthology.org/C18-1117 Tang [2018] Tang, X.: A state-of-the-art of semantic change computation. Natural Language Engineering 24(5), 649–676 (2018) Kim et al. [2014] Kim, Y., Chiu, Y.-I., Hanaki, K., Hegde, D., Petrov, S.: Temporal analysis of language through neural language models. In: Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, pp. 61–65 (2014). https://doi.org/10.3115/v1/W14-2517 . http://aclweb.org/anthology/W14-2517 Hamilton et al. [2016a] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Diachronic word embeddings reveal statistical laws of semantic change. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 1489–1501 (2016). https://doi.org/10.18653/v1/P16-1141 . http://aclweb.org/anthology/P16-1141 Hamilton et al. [2016b] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Cultural shift or linguistic drift? comparing two computational measures of semantic change. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2116–2121. Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1229 . https://www.aclweb.org/anthology/D16-1229 Yin et al. [2018] Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Kutuzov, A., Pivovarova, L.: Three-part diachronic semantic change dataset for Russian. In: Proceedings of the 2nd International Workshop on Computational Approaches to Historical Language Change 2021, pp. 7–13. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.lchange-1.2 . https://aclanthology.org/2021.lchange-1.2 Rodina and Kutuzov [2020] Rodina, J., Kutuzov, A.: RuSemShift: a dataset of historical lexical semantic change in Russian. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 1037–1047. International Committee on Computational Linguistics, Barcelona, Spain (Online) (2020). https://doi.org/10.18653/v1/2020.coling-main.90 . https://aclanthology.org/2020.coling-main.90 Martinc et al. [2022] Martinc, M., Dobrovoljc, K., Pollak, S.: Semantic change detection datasets for Slovenian 1.0. Slovenian language resource repository CLARIN.SI (2022). http://hdl.handle.net/11356/1651 Juola [2003] Juola, P.: The time course of language change. Computers and the Humanities 37(1), 77–96 (2003) Hilpert and Gries [2008] Hilpert, M., Gries, S.T.: Assessing frequency changes in multistage diachronic corpora: Applications for historical corpus linguistics and the study of language acquisition. Literary and Linguistic Computing 24(4), 385–401 (2008) Mikolov et al. [2013] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Kutuzov et al. [2018] Kutuzov, A., Øvrelid, L., Szymanski, T., Velldal, E.: Diachronic word embeddings and semantic shifts: a survey. In: Bender, E.M., Derczynski, L., Isabelle, P. (eds.) Proceedings of the 27th International Conference on Computational Linguistics, pp. 1384–1397. Association for Computational Linguistics, Santa Fe, New Mexico, USA (2018). https://aclanthology.org/C18-1117 Tang [2018] Tang, X.: A state-of-the-art of semantic change computation. Natural Language Engineering 24(5), 649–676 (2018) Kim et al. [2014] Kim, Y., Chiu, Y.-I., Hanaki, K., Hegde, D., Petrov, S.: Temporal analysis of language through neural language models. In: Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, pp. 61–65 (2014). https://doi.org/10.3115/v1/W14-2517 . http://aclweb.org/anthology/W14-2517 Hamilton et al. [2016a] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Diachronic word embeddings reveal statistical laws of semantic change. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 1489–1501 (2016). https://doi.org/10.18653/v1/P16-1141 . http://aclweb.org/anthology/P16-1141 Hamilton et al. [2016b] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Cultural shift or linguistic drift? comparing two computational measures of semantic change. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2116–2121. Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1229 . https://www.aclweb.org/anthology/D16-1229 Yin et al. [2018] Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. 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[2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. 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In: Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, pp. 61–65 (2014). https://doi.org/10.3115/v1/W14-2517 . http://aclweb.org/anthology/W14-2517 Hamilton et al. [2016a] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Diachronic word embeddings reveal statistical laws of semantic change. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 1489–1501 (2016). https://doi.org/10.18653/v1/P16-1141 . http://aclweb.org/anthology/P16-1141 Hamilton et al. [2016b] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Cultural shift or linguistic drift? comparing two computational measures of semantic change. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2116–2121. Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1229 . https://www.aclweb.org/anthology/D16-1229 Yin et al. 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International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. 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Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. 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International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. 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In: Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, pp. 61–65 (2014). https://doi.org/10.3115/v1/W14-2517 . http://aclweb.org/anthology/W14-2517 Hamilton et al. [2016a] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Diachronic word embeddings reveal statistical laws of semantic change. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 1489–1501 (2016). https://doi.org/10.18653/v1/P16-1141 . http://aclweb.org/anthology/P16-1141 Hamilton et al. [2016b] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Cultural shift or linguistic drift? comparing two computational measures of semantic change. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2116–2121. Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1229 . https://www.aclweb.org/anthology/D16-1229 Yin et al. [2018] Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. 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International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. 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Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. 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[2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. 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[2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. 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[2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. 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[2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. 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[2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. 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In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. 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International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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[2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) 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In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. 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[2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. 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In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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[2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. 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In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. 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[2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. 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[2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. 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In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. 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Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.lchange-1.2 . https://aclanthology.org/2021.lchange-1.2 Rodina and Kutuzov [2020] Rodina, J., Kutuzov, A.: RuSemShift: a dataset of historical lexical semantic change in Russian. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 1037–1047. International Committee on Computational Linguistics, Barcelona, Spain (Online) (2020). https://doi.org/10.18653/v1/2020.coling-main.90 . https://aclanthology.org/2020.coling-main.90 Martinc et al. [2022] Martinc, M., Dobrovoljc, K., Pollak, S.: Semantic change detection datasets for Slovenian 1.0. Slovenian language resource repository CLARIN.SI (2022). http://hdl.handle.net/11356/1651 Juola [2003] Juola, P.: The time course of language change. 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[2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. 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[2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. 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[2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. 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[2013] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Kutuzov et al. [2018] Kutuzov, A., Øvrelid, L., Szymanski, T., Velldal, E.: Diachronic word embeddings and semantic shifts: a survey. In: Bender, E.M., Derczynski, L., Isabelle, P. (eds.) Proceedings of the 27th International Conference on Computational Linguistics, pp. 1384–1397. Association for Computational Linguistics, Santa Fe, New Mexico, USA (2018). https://aclanthology.org/C18-1117 Tang [2018] Tang, X.: A state-of-the-art of semantic change computation. Natural Language Engineering 24(5), 649–676 (2018) Kim et al. [2014] Kim, Y., Chiu, Y.-I., Hanaki, K., Hegde, D., Petrov, S.: Temporal analysis of language through neural language models. In: Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, pp. 61–65 (2014). https://doi.org/10.3115/v1/W14-2517 . http://aclweb.org/anthology/W14-2517 Hamilton et al. [2016a] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Diachronic word embeddings reveal statistical laws of semantic change. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 1489–1501 (2016). https://doi.org/10.18653/v1/P16-1141 . http://aclweb.org/anthology/P16-1141 Hamilton et al. [2016b] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Cultural shift or linguistic drift? comparing two computational measures of semantic change. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2116–2121. Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1229 . https://www.aclweb.org/anthology/D16-1229 Yin et al. 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In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Juola, P.: The time course of language change. Computers and the Humanities 37(1), 77–96 (2003) Hilpert and Gries [2008] Hilpert, M., Gries, S.T.: Assessing frequency changes in multistage diachronic corpora: Applications for historical corpus linguistics and the study of language acquisition. Literary and Linguistic Computing 24(4), 385–401 (2008) Mikolov et al. [2013] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Kutuzov et al. [2018] Kutuzov, A., Øvrelid, L., Szymanski, T., Velldal, E.: Diachronic word embeddings and semantic shifts: a survey. In: Bender, E.M., Derczynski, L., Isabelle, P. (eds.) Proceedings of the 27th International Conference on Computational Linguistics, pp. 1384–1397. Association for Computational Linguistics, Santa Fe, New Mexico, USA (2018). https://aclanthology.org/C18-1117 Tang [2018] Tang, X.: A state-of-the-art of semantic change computation. Natural Language Engineering 24(5), 649–676 (2018) Kim et al. [2014] Kim, Y., Chiu, Y.-I., Hanaki, K., Hegde, D., Petrov, S.: Temporal analysis of language through neural language models. In: Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, pp. 61–65 (2014). https://doi.org/10.3115/v1/W14-2517 . http://aclweb.org/anthology/W14-2517 Hamilton et al. [2016a] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Diachronic word embeddings reveal statistical laws of semantic change. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 1489–1501 (2016). https://doi.org/10.18653/v1/P16-1141 . http://aclweb.org/anthology/P16-1141 Hamilton et al. [2016b] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Cultural shift or linguistic drift? comparing two computational measures of semantic change. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2116–2121. Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1229 . https://www.aclweb.org/anthology/D16-1229 Yin et al. [2018] Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Hilpert, M., Gries, S.T.: Assessing frequency changes in multistage diachronic corpora: Applications for historical corpus linguistics and the study of language acquisition. Literary and Linguistic Computing 24(4), 385–401 (2008) Mikolov et al. [2013] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Kutuzov et al. [2018] Kutuzov, A., Øvrelid, L., Szymanski, T., Velldal, E.: Diachronic word embeddings and semantic shifts: a survey. In: Bender, E.M., Derczynski, L., Isabelle, P. (eds.) Proceedings of the 27th International Conference on Computational Linguistics, pp. 1384–1397. Association for Computational Linguistics, Santa Fe, New Mexico, USA (2018). https://aclanthology.org/C18-1117 Tang [2018] Tang, X.: A state-of-the-art of semantic change computation. Natural Language Engineering 24(5), 649–676 (2018) Kim et al. [2014] Kim, Y., Chiu, Y.-I., Hanaki, K., Hegde, D., Petrov, S.: Temporal analysis of language through neural language models. In: Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, pp. 61–65 (2014). https://doi.org/10.3115/v1/W14-2517 . http://aclweb.org/anthology/W14-2517 Hamilton et al. [2016a] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Diachronic word embeddings reveal statistical laws of semantic change. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 1489–1501 (2016). https://doi.org/10.18653/v1/P16-1141 . http://aclweb.org/anthology/P16-1141 Hamilton et al. [2016b] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Cultural shift or linguistic drift? comparing two computational measures of semantic change. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2116–2121. Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1229 . https://www.aclweb.org/anthology/D16-1229 Yin et al. [2018] Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. 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IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. 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Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. 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In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. 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[2014] Kim, Y., Chiu, Y.-I., Hanaki, K., Hegde, D., Petrov, S.: Temporal analysis of language through neural language models. In: Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, pp. 61–65 (2014). https://doi.org/10.3115/v1/W14-2517 . http://aclweb.org/anthology/W14-2517 Hamilton et al. [2016a] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Diachronic word embeddings reveal statistical laws of semantic change. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 1489–1501 (2016). https://doi.org/10.18653/v1/P16-1141 . http://aclweb.org/anthology/P16-1141 Hamilton et al. [2016b] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Cultural shift or linguistic drift? comparing two computational measures of semantic change. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2116–2121. Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1229 . https://www.aclweb.org/anthology/D16-1229 Yin et al. [2018] Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Kutuzov, A., Øvrelid, L., Szymanski, T., Velldal, E.: Diachronic word embeddings and semantic shifts: a survey. In: Bender, E.M., Derczynski, L., Isabelle, P. (eds.) Proceedings of the 27th International Conference on Computational Linguistics, pp. 1384–1397. Association for Computational Linguistics, Santa Fe, New Mexico, USA (2018). https://aclanthology.org/C18-1117 Tang [2018] Tang, X.: A state-of-the-art of semantic change computation. Natural Language Engineering 24(5), 649–676 (2018) Kim et al. [2014] Kim, Y., Chiu, Y.-I., Hanaki, K., Hegde, D., Petrov, S.: Temporal analysis of language through neural language models. In: Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, pp. 61–65 (2014). https://doi.org/10.3115/v1/W14-2517 . http://aclweb.org/anthology/W14-2517 Hamilton et al. [2016a] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Diachronic word embeddings reveal statistical laws of semantic change. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 1489–1501 (2016). https://doi.org/10.18653/v1/P16-1141 . http://aclweb.org/anthology/P16-1141 Hamilton et al. [2016b] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Cultural shift or linguistic drift? comparing two computational measures of semantic change. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2116–2121. Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1229 . https://www.aclweb.org/anthology/D16-1229 Yin et al. [2018] Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. 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International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. 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In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. 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[2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Kim, Y., Chiu, Y.-I., Hanaki, K., Hegde, D., Petrov, S.: Temporal analysis of language through neural language models. In: Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, pp. 61–65 (2014). https://doi.org/10.3115/v1/W14-2517 . http://aclweb.org/anthology/W14-2517 Hamilton et al. [2016a] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Diachronic word embeddings reveal statistical laws of semantic change. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 1489–1501 (2016). https://doi.org/10.18653/v1/P16-1141 . http://aclweb.org/anthology/P16-1141 Hamilton et al. [2016b] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Cultural shift or linguistic drift? comparing two computational measures of semantic change. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2116–2121. Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1229 . https://www.aclweb.org/anthology/D16-1229 Yin et al. [2018] Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. 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Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. 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[2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. 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International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. 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[2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Hamilton, W.L., Leskovec, J., Jurafsky, D.: Cultural shift or linguistic drift? comparing two computational measures of semantic change. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2116–2121. Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1229 . https://www.aclweb.org/anthology/D16-1229 Yin et al. [2018] Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. 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[2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. 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Curran Associates Inc., Red Hook, NY, USA (2013) Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. 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Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. 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In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. 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[2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. 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[2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. 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In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. 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Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. 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In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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[2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. 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Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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[2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. 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Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. 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[2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. 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Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. 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[2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. 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[2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. 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[2013] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Kutuzov et al. [2018] Kutuzov, A., Øvrelid, L., Szymanski, T., Velldal, E.: Diachronic word embeddings and semantic shifts: a survey. In: Bender, E.M., Derczynski, L., Isabelle, P. (eds.) Proceedings of the 27th International Conference on Computational Linguistics, pp. 1384–1397. Association for Computational Linguistics, Santa Fe, New Mexico, USA (2018). https://aclanthology.org/C18-1117 Tang [2018] Tang, X.: A state-of-the-art of semantic change computation. Natural Language Engineering 24(5), 649–676 (2018) Kim et al. [2014] Kim, Y., Chiu, Y.-I., Hanaki, K., Hegde, D., Petrov, S.: Temporal analysis of language through neural language models. In: Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, pp. 61–65 (2014). https://doi.org/10.3115/v1/W14-2517 . http://aclweb.org/anthology/W14-2517 Hamilton et al. [2016a] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Diachronic word embeddings reveal statistical laws of semantic change. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 1489–1501 (2016). https://doi.org/10.18653/v1/P16-1141 . http://aclweb.org/anthology/P16-1141 Hamilton et al. [2016b] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Cultural shift or linguistic drift? comparing two computational measures of semantic change. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2116–2121. Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1229 . https://www.aclweb.org/anthology/D16-1229 Yin et al. [2018] Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. 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In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 1489–1501 (2016). https://doi.org/10.18653/v1/P16-1141 . http://aclweb.org/anthology/P16-1141 Hamilton et al. [2016b] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Cultural shift or linguistic drift? comparing two computational measures of semantic change. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2116–2121. Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1229 . https://www.aclweb.org/anthology/D16-1229 Yin et al. [2018] Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Hilpert, M., Gries, S.T.: Assessing frequency changes in multistage diachronic corpora: Applications for historical corpus linguistics and the study of language acquisition. Literary and Linguistic Computing 24(4), 385–401 (2008) Mikolov et al. [2013] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Kutuzov et al. [2018] Kutuzov, A., Øvrelid, L., Szymanski, T., Velldal, E.: Diachronic word embeddings and semantic shifts: a survey. In: Bender, E.M., Derczynski, L., Isabelle, P. (eds.) Proceedings of the 27th International Conference on Computational Linguistics, pp. 1384–1397. Association for Computational Linguistics, Santa Fe, New Mexico, USA (2018). https://aclanthology.org/C18-1117 Tang [2018] Tang, X.: A state-of-the-art of semantic change computation. Natural Language Engineering 24(5), 649–676 (2018) Kim et al. [2014] Kim, Y., Chiu, Y.-I., Hanaki, K., Hegde, D., Petrov, S.: Temporal analysis of language through neural language models. In: Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, pp. 61–65 (2014). https://doi.org/10.3115/v1/W14-2517 . http://aclweb.org/anthology/W14-2517 Hamilton et al. [2016a] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Diachronic word embeddings reveal statistical laws of semantic change. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 1489–1501 (2016). https://doi.org/10.18653/v1/P16-1141 . http://aclweb.org/anthology/P16-1141 Hamilton et al. [2016b] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Cultural shift or linguistic drift? comparing two computational measures of semantic change. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2116–2121. Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1229 . https://www.aclweb.org/anthology/D16-1229 Yin et al. [2018] Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. 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[2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. 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In: Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, pp. 61–65 (2014). https://doi.org/10.3115/v1/W14-2517 . http://aclweb.org/anthology/W14-2517 Hamilton et al. [2016a] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Diachronic word embeddings reveal statistical laws of semantic change. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 1489–1501 (2016). https://doi.org/10.18653/v1/P16-1141 . http://aclweb.org/anthology/P16-1141 Hamilton et al. [2016b] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Cultural shift or linguistic drift? comparing two computational measures of semantic change. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2116–2121. Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1229 . https://www.aclweb.org/anthology/D16-1229 Yin et al. [2018] Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. 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International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. 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[2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. 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[2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. 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In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. 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In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. 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Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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[2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. 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[2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. 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In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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[2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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[2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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[2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. 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In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. 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International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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[2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. 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Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.lchange-1.2 . https://aclanthology.org/2021.lchange-1.2 Rodina and Kutuzov [2020] Rodina, J., Kutuzov, A.: RuSemShift: a dataset of historical lexical semantic change in Russian. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 1037–1047. International Committee on Computational Linguistics, Barcelona, Spain (Online) (2020). https://doi.org/10.18653/v1/2020.coling-main.90 . https://aclanthology.org/2020.coling-main.90 Martinc et al. [2022] Martinc, M., Dobrovoljc, K., Pollak, S.: Semantic change detection datasets for Slovenian 1.0. Slovenian language resource repository CLARIN.SI (2022). http://hdl.handle.net/11356/1651 Juola [2003] Juola, P.: The time course of language change. 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In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. 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[2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. 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Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. 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In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. 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[2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Martinc, M., Dobrovoljc, K., Pollak, S.: Semantic change detection datasets for Slovenian 1.0. Slovenian language resource repository CLARIN.SI (2022). http://hdl.handle.net/11356/1651 Juola [2003] Juola, P.: The time course of language change. Computers and the Humanities 37(1), 77–96 (2003) Hilpert and Gries [2008] Hilpert, M., Gries, S.T.: Assessing frequency changes in multistage diachronic corpora: Applications for historical corpus linguistics and the study of language acquisition. Literary and Linguistic Computing 24(4), 385–401 (2008) Mikolov et al. [2013] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Kutuzov et al. [2018] Kutuzov, A., Øvrelid, L., Szymanski, T., Velldal, E.: Diachronic word embeddings and semantic shifts: a survey. In: Bender, E.M., Derczynski, L., Isabelle, P. (eds.) Proceedings of the 27th International Conference on Computational Linguistics, pp. 1384–1397. Association for Computational Linguistics, Santa Fe, New Mexico, USA (2018). https://aclanthology.org/C18-1117 Tang [2018] Tang, X.: A state-of-the-art of semantic change computation. Natural Language Engineering 24(5), 649–676 (2018) Kim et al. [2014] Kim, Y., Chiu, Y.-I., Hanaki, K., Hegde, D., Petrov, S.: Temporal analysis of language through neural language models. In: Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, pp. 61–65 (2014). https://doi.org/10.3115/v1/W14-2517 . http://aclweb.org/anthology/W14-2517 Hamilton et al. [2016a] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Diachronic word embeddings reveal statistical laws of semantic change. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 1489–1501 (2016). https://doi.org/10.18653/v1/P16-1141 . http://aclweb.org/anthology/P16-1141 Hamilton et al. [2016b] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Cultural shift or linguistic drift? comparing two computational measures of semantic change. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2116–2121. Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1229 . https://www.aclweb.org/anthology/D16-1229 Yin et al. [2018] Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. 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In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. 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Proceedings of the 27th International Conference on Computational Linguistics, pp. 1384–1397. Association for Computational Linguistics, Santa Fe, New Mexico, USA (2018). https://aclanthology.org/C18-1117 Tang [2018] Tang, X.: A state-of-the-art of semantic change computation. Natural Language Engineering 24(5), 649–676 (2018) Kim et al. [2014] Kim, Y., Chiu, Y.-I., Hanaki, K., Hegde, D., Petrov, S.: Temporal analysis of language through neural language models. In: Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, pp. 61–65 (2014). https://doi.org/10.3115/v1/W14-2517 . http://aclweb.org/anthology/W14-2517 Hamilton et al. [2016a] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Diachronic word embeddings reveal statistical laws of semantic change. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 1489–1501 (2016). https://doi.org/10.18653/v1/P16-1141 . http://aclweb.org/anthology/P16-1141 Hamilton et al. [2016b] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Cultural shift or linguistic drift? comparing two computational measures of semantic change. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2116–2121. Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1229 . https://www.aclweb.org/anthology/D16-1229 Yin et al. [2018] Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Hilpert, M., Gries, S.T.: Assessing frequency changes in multistage diachronic corpora: Applications for historical corpus linguistics and the study of language acquisition. Literary and Linguistic Computing 24(4), 385–401 (2008) Mikolov et al. [2013] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Kutuzov et al. [2018] Kutuzov, A., Øvrelid, L., Szymanski, T., Velldal, E.: Diachronic word embeddings and semantic shifts: a survey. In: Bender, E.M., Derczynski, L., Isabelle, P. (eds.) Proceedings of the 27th International Conference on Computational Linguistics, pp. 1384–1397. Association for Computational Linguistics, Santa Fe, New Mexico, USA (2018). https://aclanthology.org/C18-1117 Tang [2018] Tang, X.: A state-of-the-art of semantic change computation. Natural Language Engineering 24(5), 649–676 (2018) Kim et al. [2014] Kim, Y., Chiu, Y.-I., Hanaki, K., Hegde, D., Petrov, S.: Temporal analysis of language through neural language models. In: Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, pp. 61–65 (2014). https://doi.org/10.3115/v1/W14-2517 . http://aclweb.org/anthology/W14-2517 Hamilton et al. [2016a] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Diachronic word embeddings reveal statistical laws of semantic change. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 1489–1501 (2016). https://doi.org/10.18653/v1/P16-1141 . http://aclweb.org/anthology/P16-1141 Hamilton et al. [2016b] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Cultural shift or linguistic drift? comparing two computational measures of semantic change. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2116–2121. Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1229 . https://www.aclweb.org/anthology/D16-1229 Yin et al. [2018] Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. 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Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. 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In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Kutuzov et al. [2018] Kutuzov, A., Øvrelid, L., Szymanski, T., Velldal, E.: Diachronic word embeddings and semantic shifts: a survey. In: Bender, E.M., Derczynski, L., Isabelle, P. (eds.) Proceedings of the 27th International Conference on Computational Linguistics, pp. 1384–1397. Association for Computational Linguistics, Santa Fe, New Mexico, USA (2018). https://aclanthology.org/C18-1117 Tang [2018] Tang, X.: A state-of-the-art of semantic change computation. Natural Language Engineering 24(5), 649–676 (2018) Kim et al. [2014] Kim, Y., Chiu, Y.-I., Hanaki, K., Hegde, D., Petrov, S.: Temporal analysis of language through neural language models. In: Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, pp. 61–65 (2014). https://doi.org/10.3115/v1/W14-2517 . http://aclweb.org/anthology/W14-2517 Hamilton et al. [2016a] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Diachronic word embeddings reveal statistical laws of semantic change. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 1489–1501 (2016). https://doi.org/10.18653/v1/P16-1141 . http://aclweb.org/anthology/P16-1141 Hamilton et al. [2016b] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Cultural shift or linguistic drift? comparing two computational measures of semantic change. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2116–2121. Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1229 . https://www.aclweb.org/anthology/D16-1229 Yin et al. [2018] Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Kutuzov, A., Øvrelid, L., Szymanski, T., Velldal, E.: Diachronic word embeddings and semantic shifts: a survey. In: Bender, E.M., Derczynski, L., Isabelle, P. (eds.) Proceedings of the 27th International Conference on Computational Linguistics, pp. 1384–1397. Association for Computational Linguistics, Santa Fe, New Mexico, USA (2018). https://aclanthology.org/C18-1117 Tang [2018] Tang, X.: A state-of-the-art of semantic change computation. Natural Language Engineering 24(5), 649–676 (2018) Kim et al. [2014] Kim, Y., Chiu, Y.-I., Hanaki, K., Hegde, D., Petrov, S.: Temporal analysis of language through neural language models. In: Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, pp. 61–65 (2014). https://doi.org/10.3115/v1/W14-2517 . http://aclweb.org/anthology/W14-2517 Hamilton et al. [2016a] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Diachronic word embeddings reveal statistical laws of semantic change. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 1489–1501 (2016). https://doi.org/10.18653/v1/P16-1141 . http://aclweb.org/anthology/P16-1141 Hamilton et al. [2016b] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Cultural shift or linguistic drift? comparing two computational measures of semantic change. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2116–2121. Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1229 . https://www.aclweb.org/anthology/D16-1229 Yin et al. [2018] Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. 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[2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. 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Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Kim, Y., Chiu, Y.-I., Hanaki, K., Hegde, D., Petrov, S.: Temporal analysis of language through neural language models. In: Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, pp. 61–65 (2014). https://doi.org/10.3115/v1/W14-2517 . http://aclweb.org/anthology/W14-2517 Hamilton et al. [2016a] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Diachronic word embeddings reveal statistical laws of semantic change. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 1489–1501 (2016). https://doi.org/10.18653/v1/P16-1141 . http://aclweb.org/anthology/P16-1141 Hamilton et al. [2016b] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Cultural shift or linguistic drift? comparing two computational measures of semantic change. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2116–2121. Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1229 . https://www.aclweb.org/anthology/D16-1229 Yin et al. [2018] Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. 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[2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. 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Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. 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Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. 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[2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. 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[2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. 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IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. 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Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. 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PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. 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[2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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[2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. 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Curran Associates Inc., Red Hook, NY, USA (2013) Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. 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In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. 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Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. 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[2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. 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[2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. 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Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. 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[2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. 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[2013] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Kutuzov et al. [2018] Kutuzov, A., Øvrelid, L., Szymanski, T., Velldal, E.: Diachronic word embeddings and semantic shifts: a survey. In: Bender, E.M., Derczynski, L., Isabelle, P. (eds.) Proceedings of the 27th International Conference on Computational Linguistics, pp. 1384–1397. Association for Computational Linguistics, Santa Fe, New Mexico, USA (2018). https://aclanthology.org/C18-1117 Tang [2018] Tang, X.: A state-of-the-art of semantic change computation. Natural Language Engineering 24(5), 649–676 (2018) Kim et al. [2014] Kim, Y., Chiu, Y.-I., Hanaki, K., Hegde, D., Petrov, S.: Temporal analysis of language through neural language models. In: Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, pp. 61–65 (2014). https://doi.org/10.3115/v1/W14-2517 . http://aclweb.org/anthology/W14-2517 Hamilton et al. [2016a] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Diachronic word embeddings reveal statistical laws of semantic change. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 1489–1501 (2016). https://doi.org/10.18653/v1/P16-1141 . http://aclweb.org/anthology/P16-1141 Hamilton et al. [2016b] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Cultural shift or linguistic drift? comparing two computational measures of semantic change. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2116–2121. Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1229 . https://www.aclweb.org/anthology/D16-1229 Yin et al. [2018] Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Juola, P.: The time course of language change. Computers and the Humanities 37(1), 77–96 (2003) Hilpert and Gries [2008] Hilpert, M., Gries, S.T.: Assessing frequency changes in multistage diachronic corpora: Applications for historical corpus linguistics and the study of language acquisition. Literary and Linguistic Computing 24(4), 385–401 (2008) Mikolov et al. [2013] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Kutuzov et al. [2018] Kutuzov, A., Øvrelid, L., Szymanski, T., Velldal, E.: Diachronic word embeddings and semantic shifts: a survey. In: Bender, E.M., Derczynski, L., Isabelle, P. (eds.) Proceedings of the 27th International Conference on Computational Linguistics, pp. 1384–1397. Association for Computational Linguistics, Santa Fe, New Mexico, USA (2018). https://aclanthology.org/C18-1117 Tang [2018] Tang, X.: A state-of-the-art of semantic change computation. Natural Language Engineering 24(5), 649–676 (2018) Kim et al. [2014] Kim, Y., Chiu, Y.-I., Hanaki, K., Hegde, D., Petrov, S.: Temporal analysis of language through neural language models. In: Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, pp. 61–65 (2014). https://doi.org/10.3115/v1/W14-2517 . http://aclweb.org/anthology/W14-2517 Hamilton et al. [2016a] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Diachronic word embeddings reveal statistical laws of semantic change. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 1489–1501 (2016). https://doi.org/10.18653/v1/P16-1141 . http://aclweb.org/anthology/P16-1141 Hamilton et al. [2016b] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Cultural shift or linguistic drift? comparing two computational measures of semantic change. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2116–2121. Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1229 . https://www.aclweb.org/anthology/D16-1229 Yin et al. [2018] Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. 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International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. 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Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. 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International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Kutuzov et al. [2018] Kutuzov, A., Øvrelid, L., Szymanski, T., Velldal, E.: Diachronic word embeddings and semantic shifts: a survey. In: Bender, E.M., Derczynski, L., Isabelle, P. (eds.) Proceedings of the 27th International Conference on Computational Linguistics, pp. 1384–1397. Association for Computational Linguistics, Santa Fe, New Mexico, USA (2018). https://aclanthology.org/C18-1117 Tang [2018] Tang, X.: A state-of-the-art of semantic change computation. Natural Language Engineering 24(5), 649–676 (2018) Kim et al. 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International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. 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[2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Tang, X.: A state-of-the-art of semantic change computation. Natural Language Engineering 24(5), 649–676 (2018) Kim et al. [2014] Kim, Y., Chiu, Y.-I., Hanaki, K., Hegde, D., Petrov, S.: Temporal analysis of language through neural language models. In: Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, pp. 61–65 (2014). https://doi.org/10.3115/v1/W14-2517 . http://aclweb.org/anthology/W14-2517 Hamilton et al. [2016a] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Diachronic word embeddings reveal statistical laws of semantic change. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 1489–1501 (2016). https://doi.org/10.18653/v1/P16-1141 . http://aclweb.org/anthology/P16-1141 Hamilton et al. [2016b] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Cultural shift or linguistic drift? comparing two computational measures of semantic change. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2116–2121. Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1229 . https://www.aclweb.org/anthology/D16-1229 Yin et al. [2018] Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. 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In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. 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[2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. 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In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. 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Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. 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Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. 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Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. 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In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. 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Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. 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[2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. 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In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. 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In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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[2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. 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International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. 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Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. 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[2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. 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[2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Rodina, J., Kutuzov, A.: RuSemShift: a dataset of historical lexical semantic change in Russian. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 1037–1047. International Committee on Computational Linguistics, Barcelona, Spain (Online) (2020). https://doi.org/10.18653/v1/2020.coling-main.90 . https://aclanthology.org/2020.coling-main.90 Martinc et al. [2022] Martinc, M., Dobrovoljc, K., Pollak, S.: Semantic change detection datasets for Slovenian 1.0. Slovenian language resource repository CLARIN.SI (2022). http://hdl.handle.net/11356/1651 Juola [2003] Juola, P.: The time course of language change. Computers and the Humanities 37(1), 77–96 (2003) Hilpert and Gries [2008] Hilpert, M., Gries, S.T.: Assessing frequency changes in multistage diachronic corpora: Applications for historical corpus linguistics and the study of language acquisition. Literary and Linguistic Computing 24(4), 385–401 (2008) Mikolov et al. [2013] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Kutuzov et al. [2018] Kutuzov, A., Øvrelid, L., Szymanski, T., Velldal, E.: Diachronic word embeddings and semantic shifts: a survey. In: Bender, E.M., Derczynski, L., Isabelle, P. (eds.) Proceedings of the 27th International Conference on Computational Linguistics, pp. 1384–1397. Association for Computational Linguistics, Santa Fe, New Mexico, USA (2018). https://aclanthology.org/C18-1117 Tang [2018] Tang, X.: A state-of-the-art of semantic change computation. Natural Language Engineering 24(5), 649–676 (2018) Kim et al. [2014] Kim, Y., Chiu, Y.-I., Hanaki, K., Hegde, D., Petrov, S.: Temporal analysis of language through neural language models. In: Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, pp. 61–65 (2014). https://doi.org/10.3115/v1/W14-2517 . http://aclweb.org/anthology/W14-2517 Hamilton et al. [2016a] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Diachronic word embeddings reveal statistical laws of semantic change. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 1489–1501 (2016). https://doi.org/10.18653/v1/P16-1141 . http://aclweb.org/anthology/P16-1141 Hamilton et al. [2016b] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Cultural shift or linguistic drift? comparing two computational measures of semantic change. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2116–2121. Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1229 . https://www.aclweb.org/anthology/D16-1229 Yin et al. [2018] Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. 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[2013] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Kutuzov et al. [2018] Kutuzov, A., Øvrelid, L., Szymanski, T., Velldal, E.: Diachronic word embeddings and semantic shifts: a survey. In: Bender, E.M., Derczynski, L., Isabelle, P. (eds.) Proceedings of the 27th International Conference on Computational Linguistics, pp. 1384–1397. Association for Computational Linguistics, Santa Fe, New Mexico, USA (2018). https://aclanthology.org/C18-1117 Tang [2018] Tang, X.: A state-of-the-art of semantic change computation. Natural Language Engineering 24(5), 649–676 (2018) Kim et al. [2014] Kim, Y., Chiu, Y.-I., Hanaki, K., Hegde, D., Petrov, S.: Temporal analysis of language through neural language models. In: Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, pp. 61–65 (2014). https://doi.org/10.3115/v1/W14-2517 . http://aclweb.org/anthology/W14-2517 Hamilton et al. [2016a] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Diachronic word embeddings reveal statistical laws of semantic change. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 1489–1501 (2016). https://doi.org/10.18653/v1/P16-1141 . http://aclweb.org/anthology/P16-1141 Hamilton et al. [2016b] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Cultural shift or linguistic drift? comparing two computational measures of semantic change. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2116–2121. Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1229 . https://www.aclweb.org/anthology/D16-1229 Yin et al. [2018] Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Juola, P.: The time course of language change. Computers and the Humanities 37(1), 77–96 (2003) Hilpert and Gries [2008] Hilpert, M., Gries, S.T.: Assessing frequency changes in multistage diachronic corpora: Applications for historical corpus linguistics and the study of language acquisition. Literary and Linguistic Computing 24(4), 385–401 (2008) Mikolov et al. [2013] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Kutuzov et al. [2018] Kutuzov, A., Øvrelid, L., Szymanski, T., Velldal, E.: Diachronic word embeddings and semantic shifts: a survey. In: Bender, E.M., Derczynski, L., Isabelle, P. (eds.) Proceedings of the 27th International Conference on Computational Linguistics, pp. 1384–1397. Association for Computational Linguistics, Santa Fe, New Mexico, USA (2018). https://aclanthology.org/C18-1117 Tang [2018] Tang, X.: A state-of-the-art of semantic change computation. Natural Language Engineering 24(5), 649–676 (2018) Kim et al. [2014] Kim, Y., Chiu, Y.-I., Hanaki, K., Hegde, D., Petrov, S.: Temporal analysis of language through neural language models. In: Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, pp. 61–65 (2014). https://doi.org/10.3115/v1/W14-2517 . http://aclweb.org/anthology/W14-2517 Hamilton et al. [2016a] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Diachronic word embeddings reveal statistical laws of semantic change. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 1489–1501 (2016). https://doi.org/10.18653/v1/P16-1141 . http://aclweb.org/anthology/P16-1141 Hamilton et al. [2016b] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Cultural shift or linguistic drift? comparing two computational measures of semantic change. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2116–2121. Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1229 . https://www.aclweb.org/anthology/D16-1229 Yin et al. [2018] Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. 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International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. 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Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. 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In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Kutuzov et al. [2018] Kutuzov, A., Øvrelid, L., Szymanski, T., Velldal, E.: Diachronic word embeddings and semantic shifts: a survey. In: Bender, E.M., Derczynski, L., Isabelle, P. (eds.) Proceedings of the 27th International Conference on Computational Linguistics, pp. 1384–1397. Association for Computational Linguistics, Santa Fe, New Mexico, USA (2018). https://aclanthology.org/C18-1117 Tang [2018] Tang, X.: A state-of-the-art of semantic change computation. Natural Language Engineering 24(5), 649–676 (2018) Kim et al. 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[2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Tang, X.: A state-of-the-art of semantic change computation. Natural Language Engineering 24(5), 649–676 (2018) Kim et al. [2014] Kim, Y., Chiu, Y.-I., Hanaki, K., Hegde, D., Petrov, S.: Temporal analysis of language through neural language models. In: Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, pp. 61–65 (2014). https://doi.org/10.3115/v1/W14-2517 . http://aclweb.org/anthology/W14-2517 Hamilton et al. [2016a] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Diachronic word embeddings reveal statistical laws of semantic change. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 1489–1501 (2016). https://doi.org/10.18653/v1/P16-1141 . http://aclweb.org/anthology/P16-1141 Hamilton et al. [2016b] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Cultural shift or linguistic drift? comparing two computational measures of semantic change. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2116–2121. Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1229 . https://www.aclweb.org/anthology/D16-1229 Yin et al. [2018] Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. 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In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. 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[2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. 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[2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. 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In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. 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Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. 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International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. 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Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. 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In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. 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Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. 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In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. 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Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. 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[2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. 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In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. 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In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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[2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. 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International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. 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Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. 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In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.lchange-1.2 . https://aclanthology.org/2021.lchange-1.2 Rodina and Kutuzov [2020] Rodina, J., Kutuzov, A.: RuSemShift: a dataset of historical lexical semantic change in Russian. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 1037–1047. International Committee on Computational Linguistics, Barcelona, Spain (Online) (2020). https://doi.org/10.18653/v1/2020.coling-main.90 . https://aclanthology.org/2020.coling-main.90 Martinc et al. [2022] Martinc, M., Dobrovoljc, K., Pollak, S.: Semantic change detection datasets for Slovenian 1.0. Slovenian language resource repository CLARIN.SI (2022). http://hdl.handle.net/11356/1651 Juola [2003] Juola, P.: The time course of language change. Computers and the Humanities 37(1), 77–96 (2003) Hilpert and Gries [2008] Hilpert, M., Gries, S.T.: Assessing frequency changes in multistage diachronic corpora: Applications for historical corpus linguistics and the study of language acquisition. Literary and Linguistic Computing 24(4), 385–401 (2008) Mikolov et al. [2013] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Kutuzov et al. [2018] Kutuzov, A., Øvrelid, L., Szymanski, T., Velldal, E.: Diachronic word embeddings and semantic shifts: a survey. In: Bender, E.M., Derczynski, L., Isabelle, P. (eds.) Proceedings of the 27th International Conference on Computational Linguistics, pp. 1384–1397. Association for Computational Linguistics, Santa Fe, New Mexico, USA (2018). https://aclanthology.org/C18-1117 Tang [2018] Tang, X.: A state-of-the-art of semantic change computation. 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Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1229 . https://www.aclweb.org/anthology/D16-1229 Yin et al. [2018] Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. 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[2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. 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In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. 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[2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. 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[2013] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Kutuzov et al. [2018] Kutuzov, A., Øvrelid, L., Szymanski, T., Velldal, E.: Diachronic word embeddings and semantic shifts: a survey. In: Bender, E.M., Derczynski, L., Isabelle, P. (eds.) Proceedings of the 27th International Conference on Computational Linguistics, pp. 1384–1397. Association for Computational Linguistics, Santa Fe, New Mexico, USA (2018). https://aclanthology.org/C18-1117 Tang [2018] Tang, X.: A state-of-the-art of semantic change computation. Natural Language Engineering 24(5), 649–676 (2018) Kim et al. [2014] Kim, Y., Chiu, Y.-I., Hanaki, K., Hegde, D., Petrov, S.: Temporal analysis of language through neural language models. In: Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, pp. 61–65 (2014). https://doi.org/10.3115/v1/W14-2517 . http://aclweb.org/anthology/W14-2517 Hamilton et al. [2016a] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Diachronic word embeddings reveal statistical laws of semantic change. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 1489–1501 (2016). https://doi.org/10.18653/v1/P16-1141 . http://aclweb.org/anthology/P16-1141 Hamilton et al. [2016b] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Cultural shift or linguistic drift? comparing two computational measures of semantic change. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2116–2121. Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1229 . https://www.aclweb.org/anthology/D16-1229 Yin et al. [2018] Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. 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Proceedings of the 27th International Conference on Computational Linguistics, pp. 1384–1397. Association for Computational Linguistics, Santa Fe, New Mexico, USA (2018). https://aclanthology.org/C18-1117 Tang [2018] Tang, X.: A state-of-the-art of semantic change computation. Natural Language Engineering 24(5), 649–676 (2018) Kim et al. [2014] Kim, Y., Chiu, Y.-I., Hanaki, K., Hegde, D., Petrov, S.: Temporal analysis of language through neural language models. In: Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, pp. 61–65 (2014). https://doi.org/10.3115/v1/W14-2517 . http://aclweb.org/anthology/W14-2517 Hamilton et al. [2016a] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Diachronic word embeddings reveal statistical laws of semantic change. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 1489–1501 (2016). https://doi.org/10.18653/v1/P16-1141 . http://aclweb.org/anthology/P16-1141 Hamilton et al. [2016b] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Cultural shift or linguistic drift? comparing two computational measures of semantic change. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2116–2121. Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1229 . https://www.aclweb.org/anthology/D16-1229 Yin et al. [2018] Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Hilpert, M., Gries, S.T.: Assessing frequency changes in multistage diachronic corpora: Applications for historical corpus linguistics and the study of language acquisition. Literary and Linguistic Computing 24(4), 385–401 (2008) Mikolov et al. [2013] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Kutuzov et al. [2018] Kutuzov, A., Øvrelid, L., Szymanski, T., Velldal, E.: Diachronic word embeddings and semantic shifts: a survey. In: Bender, E.M., Derczynski, L., Isabelle, P. (eds.) Proceedings of the 27th International Conference on Computational Linguistics, pp. 1384–1397. Association for Computational Linguistics, Santa Fe, New Mexico, USA (2018). https://aclanthology.org/C18-1117 Tang [2018] Tang, X.: A state-of-the-art of semantic change computation. Natural Language Engineering 24(5), 649–676 (2018) Kim et al. [2014] Kim, Y., Chiu, Y.-I., Hanaki, K., Hegde, D., Petrov, S.: Temporal analysis of language through neural language models. In: Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, pp. 61–65 (2014). https://doi.org/10.3115/v1/W14-2517 . http://aclweb.org/anthology/W14-2517 Hamilton et al. [2016a] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Diachronic word embeddings reveal statistical laws of semantic change. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 1489–1501 (2016). https://doi.org/10.18653/v1/P16-1141 . http://aclweb.org/anthology/P16-1141 Hamilton et al. [2016b] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Cultural shift or linguistic drift? comparing two computational measures of semantic change. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2116–2121. Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1229 . https://www.aclweb.org/anthology/D16-1229 Yin et al. [2018] Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. 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[2014] Kim, Y., Chiu, Y.-I., Hanaki, K., Hegde, D., Petrov, S.: Temporal analysis of language through neural language models. In: Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, pp. 61–65 (2014). https://doi.org/10.3115/v1/W14-2517 . http://aclweb.org/anthology/W14-2517 Hamilton et al. [2016a] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Diachronic word embeddings reveal statistical laws of semantic change. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 1489–1501 (2016). https://doi.org/10.18653/v1/P16-1141 . http://aclweb.org/anthology/P16-1141 Hamilton et al. [2016b] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Cultural shift or linguistic drift? comparing two computational measures of semantic change. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2116–2121. 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[2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Kutuzov, A., Øvrelid, L., Szymanski, T., Velldal, E.: Diachronic word embeddings and semantic shifts: a survey. In: Bender, E.M., Derczynski, L., Isabelle, P. (eds.) Proceedings of the 27th International Conference on Computational Linguistics, pp. 1384–1397. Association for Computational Linguistics, Santa Fe, New Mexico, USA (2018). https://aclanthology.org/C18-1117 Tang [2018] Tang, X.: A state-of-the-art of semantic change computation. Natural Language Engineering 24(5), 649–676 (2018) Kim et al. [2014] Kim, Y., Chiu, Y.-I., Hanaki, K., Hegde, D., Petrov, S.: Temporal analysis of language through neural language models. In: Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, pp. 61–65 (2014). https://doi.org/10.3115/v1/W14-2517 . http://aclweb.org/anthology/W14-2517 Hamilton et al. [2016a] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Diachronic word embeddings reveal statistical laws of semantic change. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 1489–1501 (2016). https://doi.org/10.18653/v1/P16-1141 . http://aclweb.org/anthology/P16-1141 Hamilton et al. [2016b] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Cultural shift or linguistic drift? comparing two computational measures of semantic change. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2116–2121. Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1229 . https://www.aclweb.org/anthology/D16-1229 Yin et al. [2018] Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. 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In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. 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[2016b] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Cultural shift or linguistic drift? comparing two computational measures of semantic change. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2116–2121. Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1229 . https://www.aclweb.org/anthology/D16-1229 Yin et al. [2018] Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. 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[2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. 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In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. 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[2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. 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In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. 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[2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. 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Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. 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Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. 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International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. 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PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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[2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. 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[2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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[2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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[2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. 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Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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[2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. 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In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. 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Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. 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In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. 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Curran Associates Inc., Red Hook, NY, USA (2013) Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. 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In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. 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[2018] Kutuzov, A., Øvrelid, L., Szymanski, T., Velldal, E.: Diachronic word embeddings and semantic shifts: a survey. In: Bender, E.M., Derczynski, L., Isabelle, P. (eds.) Proceedings of the 27th International Conference on Computational Linguistics, pp. 1384–1397. Association for Computational Linguistics, Santa Fe, New Mexico, USA (2018). https://aclanthology.org/C18-1117 Tang [2018] Tang, X.: A state-of-the-art of semantic change computation. Natural Language Engineering 24(5), 649–676 (2018) Kim et al. [2014] Kim, Y., Chiu, Y.-I., Hanaki, K., Hegde, D., Petrov, S.: Temporal analysis of language through neural language models. In: Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, pp. 61–65 (2014). https://doi.org/10.3115/v1/W14-2517 . http://aclweb.org/anthology/W14-2517 Hamilton et al. [2016a] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Diachronic word embeddings reveal statistical laws of semantic change. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 1489–1501 (2016). https://doi.org/10.18653/v1/P16-1141 . http://aclweb.org/anthology/P16-1141 Hamilton et al. [2016b] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Cultural shift or linguistic drift? comparing two computational measures of semantic change. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2116–2121. Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1229 . https://www.aclweb.org/anthology/D16-1229 Yin et al. [2018] Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. 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[2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. 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In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 1489–1501 (2016). https://doi.org/10.18653/v1/P16-1141 . http://aclweb.org/anthology/P16-1141 Hamilton et al. [2016b] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Cultural shift or linguistic drift? comparing two computational measures of semantic change. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2116–2121. Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1229 . https://www.aclweb.org/anthology/D16-1229 Yin et al. [2018] Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Hilpert, M., Gries, S.T.: Assessing frequency changes in multistage diachronic corpora: Applications for historical corpus linguistics and the study of language acquisition. Literary and Linguistic Computing 24(4), 385–401 (2008) Mikolov et al. [2013] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Kutuzov et al. [2018] Kutuzov, A., Øvrelid, L., Szymanski, T., Velldal, E.: Diachronic word embeddings and semantic shifts: a survey. In: Bender, E.M., Derczynski, L., Isabelle, P. (eds.) Proceedings of the 27th International Conference on Computational Linguistics, pp. 1384–1397. Association for Computational Linguistics, Santa Fe, New Mexico, USA (2018). https://aclanthology.org/C18-1117 Tang [2018] Tang, X.: A state-of-the-art of semantic change computation. Natural Language Engineering 24(5), 649–676 (2018) Kim et al. [2014] Kim, Y., Chiu, Y.-I., Hanaki, K., Hegde, D., Petrov, S.: Temporal analysis of language through neural language models. In: Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, pp. 61–65 (2014). https://doi.org/10.3115/v1/W14-2517 . http://aclweb.org/anthology/W14-2517 Hamilton et al. [2016a] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Diachronic word embeddings reveal statistical laws of semantic change. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 1489–1501 (2016). https://doi.org/10.18653/v1/P16-1141 . http://aclweb.org/anthology/P16-1141 Hamilton et al. [2016b] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Cultural shift or linguistic drift? comparing two computational measures of semantic change. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2116–2121. Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1229 . https://www.aclweb.org/anthology/D16-1229 Yin et al. [2018] Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. 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[2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. 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In: Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, pp. 61–65 (2014). https://doi.org/10.3115/v1/W14-2517 . http://aclweb.org/anthology/W14-2517 Hamilton et al. [2016a] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Diachronic word embeddings reveal statistical laws of semantic change. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 1489–1501 (2016). https://doi.org/10.18653/v1/P16-1141 . http://aclweb.org/anthology/P16-1141 Hamilton et al. [2016b] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Cultural shift or linguistic drift? comparing two computational measures of semantic change. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2116–2121. Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1229 . https://www.aclweb.org/anthology/D16-1229 Yin et al. [2018] Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. 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[2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. 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In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. 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In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. 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Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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[2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. 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[2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. 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In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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[2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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[2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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[2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. 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In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. 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International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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[2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. 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[2018] Kutuzov, A., Øvrelid, L., Szymanski, T., Velldal, E.: Diachronic word embeddings and semantic shifts: a survey. In: Bender, E.M., Derczynski, L., Isabelle, P. (eds.) Proceedings of the 27th International Conference on Computational Linguistics, pp. 1384–1397. Association for Computational Linguistics, Santa Fe, New Mexico, USA (2018). https://aclanthology.org/C18-1117 Tang [2018] Tang, X.: A state-of-the-art of semantic change computation. Natural Language Engineering 24(5), 649–676 (2018) Kim et al. [2014] Kim, Y., Chiu, Y.-I., Hanaki, K., Hegde, D., Petrov, S.: Temporal analysis of language through neural language models. In: Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, pp. 61–65 (2014). https://doi.org/10.3115/v1/W14-2517 . http://aclweb.org/anthology/W14-2517 Hamilton et al. [2016a] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Diachronic word embeddings reveal statistical laws of semantic change. 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Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. 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PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. 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[2018] Kutuzov, A., Øvrelid, L., Szymanski, T., Velldal, E.: Diachronic word embeddings and semantic shifts: a survey. In: Bender, E.M., Derczynski, L., Isabelle, P. (eds.) Proceedings of the 27th International Conference on Computational Linguistics, pp. 1384–1397. Association for Computational Linguistics, Santa Fe, New Mexico, USA (2018). https://aclanthology.org/C18-1117 Tang [2018] Tang, X.: A state-of-the-art of semantic change computation. Natural Language Engineering 24(5), 649–676 (2018) Kim et al. [2014] Kim, Y., Chiu, Y.-I., Hanaki, K., Hegde, D., Petrov, S.: Temporal analysis of language through neural language models. In: Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, pp. 61–65 (2014). https://doi.org/10.3115/v1/W14-2517 . http://aclweb.org/anthology/W14-2517 Hamilton et al. [2016a] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Diachronic word embeddings reveal statistical laws of semantic change. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 1489–1501 (2016). https://doi.org/10.18653/v1/P16-1141 . http://aclweb.org/anthology/P16-1141 Hamilton et al. [2016b] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Cultural shift or linguistic drift? comparing two computational measures of semantic change. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2116–2121. Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1229 . https://www.aclweb.org/anthology/D16-1229 Yin et al. [2018] Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Hilpert, M., Gries, S.T.: Assessing frequency changes in multistage diachronic corpora: Applications for historical corpus linguistics and the study of language acquisition. Literary and Linguistic Computing 24(4), 385–401 (2008) Mikolov et al. [2013] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Kutuzov et al. [2018] Kutuzov, A., Øvrelid, L., Szymanski, T., Velldal, E.: Diachronic word embeddings and semantic shifts: a survey. In: Bender, E.M., Derczynski, L., Isabelle, P. (eds.) Proceedings of the 27th International Conference on Computational Linguistics, pp. 1384–1397. Association for Computational Linguistics, Santa Fe, New Mexico, USA (2018). https://aclanthology.org/C18-1117 Tang [2018] Tang, X.: A state-of-the-art of semantic change computation. Natural Language Engineering 24(5), 649–676 (2018) Kim et al. [2014] Kim, Y., Chiu, Y.-I., Hanaki, K., Hegde, D., Petrov, S.: Temporal analysis of language through neural language models. In: Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, pp. 61–65 (2014). https://doi.org/10.3115/v1/W14-2517 . http://aclweb.org/anthology/W14-2517 Hamilton et al. [2016a] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Diachronic word embeddings reveal statistical laws of semantic change. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 1489–1501 (2016). https://doi.org/10.18653/v1/P16-1141 . http://aclweb.org/anthology/P16-1141 Hamilton et al. [2016b] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Cultural shift or linguistic drift? comparing two computational measures of semantic change. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2116–2121. Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1229 . https://www.aclweb.org/anthology/D16-1229 Yin et al. [2018] Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. 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IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. 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Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. 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In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. 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[2014] Kim, Y., Chiu, Y.-I., Hanaki, K., Hegde, D., Petrov, S.: Temporal analysis of language through neural language models. In: Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, pp. 61–65 (2014). https://doi.org/10.3115/v1/W14-2517 . http://aclweb.org/anthology/W14-2517 Hamilton et al. [2016a] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Diachronic word embeddings reveal statistical laws of semantic change. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 1489–1501 (2016). https://doi.org/10.18653/v1/P16-1141 . http://aclweb.org/anthology/P16-1141 Hamilton et al. [2016b] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Cultural shift or linguistic drift? comparing two computational measures of semantic change. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2116–2121. Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1229 . https://www.aclweb.org/anthology/D16-1229 Yin et al. [2018] Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Kutuzov, A., Øvrelid, L., Szymanski, T., Velldal, E.: Diachronic word embeddings and semantic shifts: a survey. In: Bender, E.M., Derczynski, L., Isabelle, P. (eds.) Proceedings of the 27th International Conference on Computational Linguistics, pp. 1384–1397. Association for Computational Linguistics, Santa Fe, New Mexico, USA (2018). https://aclanthology.org/C18-1117 Tang [2018] Tang, X.: A state-of-the-art of semantic change computation. Natural Language Engineering 24(5), 649–676 (2018) Kim et al. [2014] Kim, Y., Chiu, Y.-I., Hanaki, K., Hegde, D., Petrov, S.: Temporal analysis of language through neural language models. In: Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, pp. 61–65 (2014). https://doi.org/10.3115/v1/W14-2517 . http://aclweb.org/anthology/W14-2517 Hamilton et al. [2016a] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Diachronic word embeddings reveal statistical laws of semantic change. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 1489–1501 (2016). https://doi.org/10.18653/v1/P16-1141 . http://aclweb.org/anthology/P16-1141 Hamilton et al. [2016b] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Cultural shift or linguistic drift? comparing two computational measures of semantic change. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2116–2121. Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1229 . https://www.aclweb.org/anthology/D16-1229 Yin et al. [2018] Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. 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International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. 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In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. 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[2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Kim, Y., Chiu, Y.-I., Hanaki, K., Hegde, D., Petrov, S.: Temporal analysis of language through neural language models. In: Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, pp. 61–65 (2014). https://doi.org/10.3115/v1/W14-2517 . http://aclweb.org/anthology/W14-2517 Hamilton et al. [2016a] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Diachronic word embeddings reveal statistical laws of semantic change. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 1489–1501 (2016). https://doi.org/10.18653/v1/P16-1141 . http://aclweb.org/anthology/P16-1141 Hamilton et al. [2016b] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Cultural shift or linguistic drift? comparing two computational measures of semantic change. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2116–2121. Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1229 . https://www.aclweb.org/anthology/D16-1229 Yin et al. [2018] Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. 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Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. 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[2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. 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International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. 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[2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Hamilton, W.L., Leskovec, J., Jurafsky, D.: Cultural shift or linguistic drift? comparing two computational measures of semantic change. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2116–2121. Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1229 . https://www.aclweb.org/anthology/D16-1229 Yin et al. [2018] Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. 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[2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. 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Curran Associates Inc., Red Hook, NY, USA (2013) Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. 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Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. 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In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. 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[2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. 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[2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. 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In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. 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Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. 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In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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[2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. 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Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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[2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. 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Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. 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[2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. 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Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. 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[2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. 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[2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. 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In: Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, pp. 61–65 (2014). https://doi.org/10.3115/v1/W14-2517 . http://aclweb.org/anthology/W14-2517 Hamilton et al. [2016a] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Diachronic word embeddings reveal statistical laws of semantic change. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 1489–1501 (2016). https://doi.org/10.18653/v1/P16-1141 . http://aclweb.org/anthology/P16-1141 Hamilton et al. [2016b] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Cultural shift or linguistic drift? comparing two computational measures of semantic change. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2116–2121. Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1229 . https://www.aclweb.org/anthology/D16-1229 Yin et al. 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International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. 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Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. 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International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. 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In: Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, pp. 61–65 (2014). https://doi.org/10.3115/v1/W14-2517 . http://aclweb.org/anthology/W14-2517 Hamilton et al. [2016a] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Diachronic word embeddings reveal statistical laws of semantic change. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 1489–1501 (2016). https://doi.org/10.18653/v1/P16-1141 . http://aclweb.org/anthology/P16-1141 Hamilton et al. [2016b] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Cultural shift or linguistic drift? comparing two computational measures of semantic change. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2116–2121. Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1229 . https://www.aclweb.org/anthology/D16-1229 Yin et al. [2018] Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. 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International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. 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Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. 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[2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. 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[2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. 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[2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. 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[2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. 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[2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. 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In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. 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International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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[2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) 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In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. 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[2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. 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In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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[2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. 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In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. 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[2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. 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[2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. 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In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. 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[2018] Kutuzov, A., Øvrelid, L., Szymanski, T., Velldal, E.: Diachronic word embeddings and semantic shifts: a survey. In: Bender, E.M., Derczynski, L., Isabelle, P. (eds.) Proceedings of the 27th International Conference on Computational Linguistics, pp. 1384–1397. Association for Computational Linguistics, Santa Fe, New Mexico, USA (2018). https://aclanthology.org/C18-1117 Tang [2018] Tang, X.: A state-of-the-art of semantic change computation. Natural Language Engineering 24(5), 649–676 (2018) Kim et al. [2014] Kim, Y., Chiu, Y.-I., Hanaki, K., Hegde, D., Petrov, S.: Temporal analysis of language through neural language models. In: Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, pp. 61–65 (2014). https://doi.org/10.3115/v1/W14-2517 . http://aclweb.org/anthology/W14-2517 Hamilton et al. [2016a] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Diachronic word embeddings reveal statistical laws of semantic change. 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Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. 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[2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Hilpert, M., Gries, S.T.: Assessing frequency changes in multistage diachronic corpora: Applications for historical corpus linguistics and the study of language acquisition. Literary and Linguistic Computing 24(4), 385–401 (2008) Mikolov et al. [2013] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Kutuzov et al. [2018] Kutuzov, A., Øvrelid, L., Szymanski, T., Velldal, E.: Diachronic word embeddings and semantic shifts: a survey. In: Bender, E.M., Derczynski, L., Isabelle, P. (eds.) Proceedings of the 27th International Conference on Computational Linguistics, pp. 1384–1397. Association for Computational Linguistics, Santa Fe, New Mexico, USA (2018). https://aclanthology.org/C18-1117 Tang [2018] Tang, X.: A state-of-the-art of semantic change computation. Natural Language Engineering 24(5), 649–676 (2018) Kim et al. [2014] Kim, Y., Chiu, Y.-I., Hanaki, K., Hegde, D., Petrov, S.: Temporal analysis of language through neural language models. In: Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, pp. 61–65 (2014). https://doi.org/10.3115/v1/W14-2517 . http://aclweb.org/anthology/W14-2517 Hamilton et al. [2016a] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Diachronic word embeddings reveal statistical laws of semantic change. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 1489–1501 (2016). https://doi.org/10.18653/v1/P16-1141 . http://aclweb.org/anthology/P16-1141 Hamilton et al. [2016b] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Cultural shift or linguistic drift? comparing two computational measures of semantic change. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2116–2121. Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1229 . https://www.aclweb.org/anthology/D16-1229 Yin et al. [2018] Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. 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[2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. 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In: Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, pp. 61–65 (2014). https://doi.org/10.3115/v1/W14-2517 . http://aclweb.org/anthology/W14-2517 Hamilton et al. [2016a] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Diachronic word embeddings reveal statistical laws of semantic change. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 1489–1501 (2016). https://doi.org/10.18653/v1/P16-1141 . http://aclweb.org/anthology/P16-1141 Hamilton et al. [2016b] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Cultural shift or linguistic drift? comparing two computational measures of semantic change. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2116–2121. Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1229 . https://www.aclweb.org/anthology/D16-1229 Yin et al. 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In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. 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[2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. 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[2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. 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[2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Hamilton, W.L., Leskovec, J., Jurafsky, D.: Cultural shift or linguistic drift? comparing two computational measures of semantic change. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2116–2121. Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1229 . https://www.aclweb.org/anthology/D16-1229 Yin et al. [2018] Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. 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In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. 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International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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[2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) 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In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. 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[2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. 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In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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[2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. 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In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. 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[2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. 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[2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. 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In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. 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In: Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, pp. 61–65 (2014). https://doi.org/10.3115/v1/W14-2517 . http://aclweb.org/anthology/W14-2517 Hamilton et al. [2016a] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Diachronic word embeddings reveal statistical laws of semantic change. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 1489–1501 (2016). https://doi.org/10.18653/v1/P16-1141 . http://aclweb.org/anthology/P16-1141 Hamilton et al. [2016b] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Cultural shift or linguistic drift? comparing two computational measures of semantic change. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2116–2121. Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1229 . https://www.aclweb.org/anthology/D16-1229 Yin et al. 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International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. 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Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. 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International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. 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[2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. 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Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1229 . https://www.aclweb.org/anthology/D16-1229 Yin et al. [2018] Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. 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In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. 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[2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. 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[2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. 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Curran Associates Inc., Red Hook, NY, USA (2013) Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. 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International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. 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Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. 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Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. 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[2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. 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Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. 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Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. 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[2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. 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In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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[2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. 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[2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. 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Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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[2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. 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[2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. 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Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. 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The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. 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[2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. 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Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 26 (2013) Kutuzov et al. [2018] Kutuzov, A., Øvrelid, L., Szymanski, T., Velldal, E.: Diachronic word embeddings and semantic shifts: a survey. In: Bender, E.M., Derczynski, L., Isabelle, P. (eds.) Proceedings of the 27th International Conference on Computational Linguistics, pp. 1384–1397. Association for Computational Linguistics, Santa Fe, New Mexico, USA (2018). https://aclanthology.org/C18-1117 Tang [2018] Tang, X.: A state-of-the-art of semantic change computation. Natural Language Engineering 24(5), 649–676 (2018) Kim et al. [2014] Kim, Y., Chiu, Y.-I., Hanaki, K., Hegde, D., Petrov, S.: Temporal analysis of language through neural language models. In: Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, pp. 61–65 (2014). https://doi.org/10.3115/v1/W14-2517 . http://aclweb.org/anthology/W14-2517 Hamilton et al. [2016a] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Diachronic word embeddings reveal statistical laws of semantic change. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 1489–1501 (2016). https://doi.org/10.18653/v1/P16-1141 . http://aclweb.org/anthology/P16-1141 Hamilton et al. [2016b] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Cultural shift or linguistic drift? comparing two computational measures of semantic change. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2116–2121. Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1229 . https://www.aclweb.org/anthology/D16-1229 Yin et al. [2018] Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. 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In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 1489–1501 (2016). https://doi.org/10.18653/v1/P16-1141 . http://aclweb.org/anthology/P16-1141 Hamilton et al. [2016b] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Cultural shift or linguistic drift? comparing two computational measures of semantic change. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2116–2121. Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1229 . https://www.aclweb.org/anthology/D16-1229 Yin et al. [2018] Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. 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Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. 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[2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. 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[2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. 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In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. 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In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. 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[2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. 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[2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. 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Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. 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International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. 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[2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. 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In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. 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PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. 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In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. 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[2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. 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In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. 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In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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[2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. 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In: Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, pp. 61–65 (2014). https://doi.org/10.3115/v1/W14-2517 . http://aclweb.org/anthology/W14-2517 Hamilton et al. [2016a] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Diachronic word embeddings reveal statistical laws of semantic change. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pp. 1489–1501 (2016). https://doi.org/10.18653/v1/P16-1141 . http://aclweb.org/anthology/P16-1141 Hamilton et al. [2016b] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Cultural shift or linguistic drift? comparing two computational measures of semantic change. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2116–2121. Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1229 . https://www.aclweb.org/anthology/D16-1229 Yin et al. 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International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. 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[2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. 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[2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. 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International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. 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Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. 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In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. 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[2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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[2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. 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[2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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[2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. 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In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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[2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. 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Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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[2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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[2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. 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In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. 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In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. 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Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. 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Curran Associates Inc., Red Hook, NY, USA (2013) Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. 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In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. 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In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. 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[2016b] Hamilton, W.L., Leskovec, J., Jurafsky, D.: Cultural shift or linguistic drift? comparing two computational measures of semantic change. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2116–2121. Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1229 . https://www.aclweb.org/anthology/D16-1229 Yin et al. [2018] Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. 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[2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. 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In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. 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[2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. 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In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. 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[2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. 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Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. 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Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. 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International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. 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PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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[2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. 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[2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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[2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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[2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. 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Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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[2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. 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In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. 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Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. 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In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. 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Curran Associates Inc., Red Hook, NY, USA (2013) Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. 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In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. 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[2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. 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[2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. 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In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. 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Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. 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Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. 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[2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. 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Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. 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In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. 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In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. 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Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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[2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. 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In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. 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The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. 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The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. 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[2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. 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[2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. 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In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. 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Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. 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International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. 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Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. 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In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. 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Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. 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In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. 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Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. 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[2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. 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In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. 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In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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[2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. 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International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. 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Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. 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Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1229 . https://www.aclweb.org/anthology/D16-1229 Yin et al. [2018] Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Hamilton, W.L., Leskovec, J., Jurafsky, D.: Cultural shift or linguistic drift? comparing two computational measures of semantic change. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2116–2121. Association for Computational Linguistics, Austin, Texas (2016). https://doi.org/10.18653/v1/D16-1229 . https://www.aclweb.org/anthology/D16-1229 Yin et al. [2018] Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. 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[2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Yin, Z., Sachidananda, V., Prabhakar, B.: The global anchor method for quantifying linguistic shifts and domain adaptation. In: Advances in Neural Information Processing Systems, pp. 9412–9423 (2018) Gonen et al. [2020] Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. 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International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. 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[2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. 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University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. 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PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. 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[2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. 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Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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[2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. 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Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. 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[2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. 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In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. 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In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. 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[2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. 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[2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. 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Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. 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[2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. 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[2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. 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Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. 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Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. 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In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. 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[2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. 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Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. 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[2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. 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In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. 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In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. 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[2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. 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The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Gonen, H., Jawahar, G., Seddah, D., Goldberg, Y.: Simple, interpretable and stable method for detecting words with usage change across corpora. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 538–555. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.51 Frermann and Lapata [2016] Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. 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[2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. 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[2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. 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Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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[2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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[2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. 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In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. 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International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Frermann, L., Lapata, M.: A Bayesian model of diachronic meaning change. Transactions of the Association for Computational Linguistics 4, 31–45 (2016) https://doi.org/10.1162/tacl_a_00081 Devlin et al. [2019] Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://www.aclweb.org/anthology/N19-1423 Martinc et al. [2020] Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. 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[2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. 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[2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. 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Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. 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[2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. 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Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. 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[2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. 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Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. 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The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. 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[2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Martinc, M., Kralj Novak, P., Pollak, S.: Leveraging contextual embeddings for detecting diachronic semantic shift. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4811–4819. European Language Resources Association, Marseille, France (2020). https://aclanthology.org/2020.lrec-1.592 Kutuzov and Giulianelli [2020] Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. 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Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. 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In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. 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[2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. 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Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. 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In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. 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Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. 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Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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[2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. 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In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. 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In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. 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[2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. 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International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. 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[2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. 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PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. 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In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. 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[2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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[2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. 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In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. 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In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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[2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. 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International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Kutuzov, A., Giulianelli, M.: UiO-UvA at SemEval-2020 task 1: Contextualised embeddings for lexical semantic change detection. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 126–134. International Committee for Computational Linguistics, Barcelona (online) (2020). https://www.aclweb.org/anthology/2020.semeval-1.14 Giulianelli et al. [2020] Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. 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[2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. 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In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. 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PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. 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In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. 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[2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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[2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. 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In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. 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In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. 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Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. 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[2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. 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Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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[2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. 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Curran Associates Inc., Red Hook, NY, USA (2013) Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. 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In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. 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Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. 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[2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. 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[2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Giulianelli, M., Del Tredici, M., Fernández, R.: Analysing lexical semantic change with contextualised word representations. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3960–3973. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.acl-main.365 Martinc et al. [2020] Martinc, M., Montariol, S., Zosa, E., Pivovarova, L.: Capturing evolution in word usage: Just add more clusters? In: Companion Proceedings of the Web Conference 2020. WWW ’20, pp. 343–349. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366424.3382186 . https://doi.org/10.1145/3366424.3382186 Lin [1991] Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. 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Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information theory 37(1), 145–151 (1991) Solomon [2018] Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. 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In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. 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Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. 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In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. 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In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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[2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. 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Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. 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In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. 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Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. 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In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. 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[2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. 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In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. 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Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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[2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. 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Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. 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In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. 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[2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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[2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. 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In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. 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Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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[2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. 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[2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. 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Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Solomon, J.: Optimal transport on discrete domains (2018) Zhou et al. [2023] Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. 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In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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[2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. 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Curran Associates Inc., Red Hook, NY, USA (2013) Zhou, W., Tahmasebi, N., Dubossarsky, H.: The finer they get: Combining fine-tuned models for better semantic change detection. In: Alumäe, T., Fishel, M. (eds.) Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pp. 518–528. University of Tartu Library, Tórshavn, Faroe Islands (2023). https://aclanthology.org/2023.nodalida-1.52 Pfeiffer et al. [2020] Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. 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In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Pfeiffer, J., Rücklé, A., Poth, C., Kamath, A., Vulić, I., Ruder, S., Cho, K., Gurevych, I.: Adapterhub: A framework for adapting transformers. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020): Systems Demonstrations, pp. 46–54. Association for Computational Linguistics, Online (2020). https://www.aclweb.org/anthology/2020.emnlp-demos.7 Card [2023] Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Card, D.: Substitution-based semantic change detection using contextual embeddings. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 590–602. 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Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. 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[2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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[2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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[2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. 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Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. 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[2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. 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Association for Computational Linguistics, Toronto, Canada (2023). https://doi.org/10.18653/v1/2023.acl-short.52 . https://aclanthology.org/2023.acl-short.52 Monge [1781] Monge, G.: Mémoire sur la Théorie des Déblais Et des remblais. De l’Imprimerie Royale, Paris, France (1781) Li et al. [2020] Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. 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In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. 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In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. 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In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. 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In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. 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Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Li, J., Li, C., Wang, G., Fu, H., Lin, Y., Chen, L., Zhang, Y., Tao, C., Zhang, R., Wang, W., Shen, D., Yang, Q., Carin, L.: Improving text generation with student-forcing optimal transport. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9144–9156. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.735 . https://aclanthology.org/2020.emnlp-main.735 Swanson et al. [2020] Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. 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In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. 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Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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[2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. 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In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. 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Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. 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Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Swanson, K., Yu, L., Lei, T.: Rationalizing text matching: Learning sparse alignments via optimal transport. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5609–5626. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.496 . https://aclanthology.org/2020.acl-main.496 Xu et al. [2021] Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. 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International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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[2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. 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In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. 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[2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Xu, J., Zhou, H., Gan, C., Zheng, Z., Li, L.: Vocabulary learning via optimal transport for neural machine translation. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 7361–7373. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.571 . https://aclanthology.org/2021.acl-long.571 Kusner et al. [2015] Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. 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Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 957–966. PMLR, Lille, France (2015). https://proceedings.mlr.press/v37/kusnerb15.html Zhao et al. [2019] Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. 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Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. 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Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. 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In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. 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Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. 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Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. 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In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. 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In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Zhao, W., Peyrard, M., Liu, F., Gao, Y., Meyer, C.M., Eger, S.: MoverScore: Text generation evaluating with contextualized embeddings and earth mover distance. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 563–578. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1053 . https://aclanthology.org/D19-1053 Lee et al. [2022] Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Lee, S., Lee, D., Jang, S., Yu, H.: Toward interpretable semantic textual similarity via optimal transport-based contrastive sentence learning. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5969–5979. Association for Computational Linguistics, Dublin, Ireland (2022). https://doi.org/10.18653/v1/2022.acl-long.412 . https://aclanthology.org/2022.acl-long.412 Pollak et al. [2019] Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Pollak, S., Gantar, P., Arhar Holdt, Š.: What’s New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene. International Journal of Lexicography 32(2), 184–206 (2019) https://doi.org/10.1093/ijl/ecy026 Fišer and Ljubešić [2019] Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. 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Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. 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In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. 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In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. 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Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. 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Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. 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Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Fišer, D., Ljubešić, N.: Distributional modelling for semantic shift detection. International journal of lexicography 32(2), 163–183 (2019) Gantar et al. [2018] Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. 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Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Gantar, P., Holdt, Š.A., Pollak, S.: Leksikalne novosti v besedilih računalniško posredovane komunikacije. Slavisticna Revija 66(4), 459–472 (2018) Schlechtweg et al. [2019] Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. 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[2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. 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Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013)
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In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. 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[2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013)
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Curran Associates Inc., Red Hook, NY, USA (2013) Schlechtweg, D., Hätty, A., Del Tredici, M., Walde, S.: A wind of change: Detecting and evaluating lexical semantic change across times and domains. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 732–746. Association for Computational Linguistics, Florence, Italy (2019). https://www.aclweb.org/anthology/P19-1072 Hanks [2013] Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. 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Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Hanks, P.: Lexical Analysis: Norms and Exploitations. The MIT Press, Cambridge, MA, USA (2013) Reimers and Gurevych [2019] Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Reimers, N., Gurevych, I.: Sentence-BERT: Sentence embeddings using siamese BERT-networks. ArXiv abs/1908.10084 (2019) Zhang et al. [2020] Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. 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[2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. 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Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. 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Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013)
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Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013)
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[2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating text generation with BERT. ArXiv abs/1904.09675 (2020) Liu et al. [2019] Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. 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Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. 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In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013)
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In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013)
- Liu, N.F., Gardner, M., Belinkov, Y., Peters, M.E., Smith, N.A.: Linguistic knowledge and transferability of contextual representations. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 1073–1094. Association for Computational Linguistics, Minneapolis, Minnesota (2019) Coenen et al. [2019] Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013)
- Coenen, A., Reif, E., Yuan, A., Kim, B., Pearce, A., Viégas, F., Wattenberg, M.: Visualizing and measuring the geometry of BERT. Curran Associates Inc., Red Hook, NY, USA (2019) Turton et al. [2021] Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013)
- Turton, J., Smith, R.E., Vinson, D.: Deriving contextualised semantic features from BERT (and other transformer model) embeddings. In: Rogers, A., Calixto, I., Vulić, I., Saphra, N., Kassner, N., Camburu, O.-M., Bansal, T., Shwartz, V. (eds.) Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021), pp. 248–262. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.repl4nlp-1.26 . https://aclanthology.org/2021.repl4nlp-1.26 Ulčar and Robnik-Šikonja [2021] Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013)
- Ulčar, M., Robnik-Šikonja, M.: SloBERTa: Slovene monolingual large pretrained masked language model (2021) Flamary et al. [2021] Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013)
- Flamary, R., Courty, N., Gramfort, A., Alaya, M.Z., Boisbunon, A., Chambon, S., Chapel, L., Corenflos, A., Fatras, K., Fournier, N., Gautheron, L., Gayraud, N.T.H., Janati, H., Rakotomamonjy, A., Redko, I., Rolet, A., Schutz, A., Seguy, V., Sutherland, D.J., Tavenard, R., Tong, A., Vayer, T.: Pot: Python optimal transport. Journal of Machine Learning Research 22(78), 1–8 (2021) Cuturi [2013] Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013) Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013)
- Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS’13, pp. 2292–2300. Curran Associates Inc., Red Hook, NY, USA (2013)